Complaint CodedMonth DateOccur FlagCrime FlagUnfounded
1: 19-000184 2018-12 12/31/2018 15:30 Y NA
2: 18-061560 2018-12 12/31/2018 15:30 Y NA
3: 18-061560 2018-12 12/31/2018 15:30 Y NA
4: 18-061554 2018-12 12/31/2018 15:00 Y NA
5: 18-061561 2018-12 12/31/2018 14:45 Y NA
6: 19-000083 2018-12 12/31/2018 14:30 Y NA
FlagAdministrative Count FlagCleanup Crime District
1: NA 1 NA 64601 3
2: NA 1 NA 91123 1
3: NA 1 NA 91113 1
4: NA 1 NA 265321 6
5: NA 1 NA 64701 5
6: NA 1 NA 64701 1
Description ILEADSAddress
1: LARCENY-FROM MTR VEH $500 - $24,999 0
2: SIMPLE ASSAULT-CHILD/NO INJURY 4117
3: SIMPLE ASSAULT-ADULT/NO INJURY 4117
4: LEAVING SCENE OF ACCIDENT 0
5: LARCENY-FROM MTR VEH UNDER $500 0
6: LARCENY-FROM MTR VEH UNDER $500 2920
ILEADSStreet Neighborhood LocationName LocationComment
1: S 3RD ST / GEYER AVE 20
2: GERMANIA ST 4
3: GERMANIA ST 4
4: N KINGSHIGHWAY BLVD / NATURAL 55
5: ANNIE MALONE DR / W SAINT FERD 57
6: MERAMEC ST 17 US POST OFFICE
CADAddress CADStreet XCoord YCoord
1: NA 906024.9 1009980.0
2: 4117 GERMANIA 882747.7 992739.9
3: 4117 GERMANIA 882747.7 992739.9
4: 4223 ENRIGHT 891049.0 1035207.0
5: NA 895101.4 1028721.0
6: 5410 IDAHO 895897.0 1000157.0
The STL Metropolitan Police produces a monthly crime update.
Stored in a csv format and can be downloaded.
Located at https://www.slmpd.org/Crimereports.shtml.
The file provides all crime details collected from the preceding month.
Contains locations, neighborhoods, precincts, map coordinates and times of crimes in the St Louis Metropolitan Area.
Complaint CodedMonth DateOccur FlagCrime
Length:91869 Length:91869 Length:91869 Length:91869
Class :character Class :character Class :character Class :character
Mode :character Mode :character Mode :character Mode :character
FlagUnfounded FlagAdministrative Count FlagCleanup
Mode:logical Mode:logical Min. :-1.0000 Mode:logical
NA's:91869 NA's:91869 1st Qu.: 1.0000 NA's:91869
Median : 1.0000
Mean : 0.9744
3rd Qu.: 1.0000
Max. : 1.0000
Crime District Description ILEADSAddress
Min. : 10000 Min. :0.00 Length:91869 Min. : 0
1st Qu.: 64701 1st Qu.:2.00 Class :character 1st Qu.: 900
Median : 71030 Median :4.00 Mode :character Median : 3301
Mean :122465 Mean :3.63 Mean : 3006
3rd Qu.:182260 3rd Qu.:5.00 3rd Qu.: 4611
Max. :266999 Max. :6.00 Max. :12361
NA's :137
ILEADSStreet Neighborhood LocationName LocationComment
Length:91869 Min. : 0.00 Length:91869 Length:91869
Class :character 1st Qu.:17.00 Class :character Class :character
Mode :character Median :36.00 Mode :character Mode :character
Mean :38.87
3rd Qu.:59.00
Max. :88.00
CADAddress CADStreet XCoord YCoord
Min. : 0 Length:91869 Min. : 0 Min. : 0
1st Qu.: 1709 Class :character 1st Qu.:887686 1st Qu.:1005015
Median : 3755 Mode :character Median :893529 Median :1018473
Mean : 3554 Mean :870667 Mean : 991942
3rd Qu.: 4930 3rd Qu.:900048 3rd Qu.:1029507
Max. :13915 Max. :911342 Max. :1093729
NA's :22092
Again, some fields are irrelevant to our analysis.
We will remove these elements using a tidyverse library called dplyr.
We will also have to restructure certain date/time variables.
Flags are not needed.
Don’t see how count field is significant in the analysis.
Observations: 358
Variables: 15
$ Complaint <chr> "18-061082", "18-060906", "18-060607", "18-060518",...
$ CodedMonth <chr> "2018-12", "2018-12", "2018-12", "2018-12", "2018-1...
$ DateOccur <chr> "12/28/2018 1:15", "12/26/2018 21:56", "12/24/2018 ...
$ Crime <int> 10000, 10000, 10000, 10000, 10000, 10000, 10000, 10...
$ District <int> 6, 3, 6, 1, 6, 5, 6, 4, 3, 4, 5, 6, 6, 3, 2, 6, 6, ...
$ Description <chr> "HOMICIDE", "HOMICIDE", "HOMICIDE", "HOMICIDE", "HO...
$ ILEADSAddress <int> 5300, 2651, 4042, 5913, 4476, 1224, 3921, 0, 1051, ...
$ ILEADSStreet <chr> "GERALDINE AVE", "HICKORY ST", "CARTER AVE", "PENNS...
$ Neighborhood <int> 71, 31, 68, 1, 56, 53, 67, 63, 21, 60, 55, 84, 76, ...
$ LocationName <chr> "", "CAROLINE PLACE APARTMENTS", "", "", "", "", ""...
$ LocationComment <chr> "", "APARTMENT COMPLEX", "", "", "", "", "", "", ""...
$ CADAddress <int> 5304, 2654, NA, 5913, NA, 1200, 3927, NA, 1051, 193...
$ CADStreet <chr> "GERALDINE", "RUTGER", "", "PENNSYLVANIA", "", "AUB...
$ XCoord <dbl> 893346.5, 900117.9, 899795.6, 892982.7, 894410.2, 8...
$ YCoord <dbl> 1040599.0, 1015110.0, 1033658.0, 992870.4, 1032898....
I wanted to select a specific crime. In this case we will look at Homicides.
Some data fields are not relevant to the analysis so I’ve limited the data to the following 6 elements.
Homicides are UCR coded as 10000.
Although the STLMPD website states rows are unique, they are NOT.
During this phase I also wanted to determine data types.
The mix is a combination of characters string and integers.
I will have to re-charactize some elements to more easily manipulate later.
“CodedMonth” and “DateOccur” are not date/time elements, so they need to be changed.
'data.frame': 358 obs. of 15 variables:
$ Complaint : chr "18-061082" "18-060906" "18-060607" "18-060518" ...
$ CodedMonth : Date, format: "2018-12-28" "2018-12-28" ...
$ DateOccur : POSIXct, format: "2018-12-28 01:15:00" "2018-12-26 21:56:00" ...
$ Crime : int 10000 10000 10000 10000 10000 10000 10000 10000 10000 10000 ...
$ District : int 6 3 6 1 6 5 6 4 3 4 ...
$ Description : chr "HOMICIDE" "HOMICIDE" "HOMICIDE" "HOMICIDE" ...
$ ILEADSAddress : int 5300 2651 4042 5913 4476 1224 3921 0 1051 1933 ...
$ ILEADSStreet : chr "GERALDINE AVE" "HICKORY ST" "CARTER AVE" "PENNSYLVANIA AVE" ...
$ Neighborhood : int 71 31 68 1 56 53 67 63 21 60 ...
$ LocationName : chr "" "CAROLINE PLACE APARTMENTS" "" "" ...
$ LocationComment: chr "" "APARTMENT COMPLEX" "" "" ...
$ CADAddress : int 5304 2654 NA 5913 NA 1200 3927 NA 1051 1933 ...
$ CADStreet : chr "GERALDINE" "RUTGER" "" "PENNSYLVANIA" ...
$ XCoord : num 893347 900118 899796 892983 894410 ...
$ YCoord : num 1040599 1015110 1033658 992870 1032898 ...
Need to use some R libraries to convert data types.
Used stringr and lubridate libraries to change data types.
Changed “CodedMonth” to a string value closer to one resembling a year/month/day field.
Used 28 days as the day value so I do not have to constantly worry about the changing days/month values.
Since the data is collected as of the last day of the month, it will not affect the monthly crime perspective.
Next I created a concatonated string group and convert that field into a “POSIX” day/month/day variable.
Reporting.diff YCoord XCoord CADStreet CADAddress
1 749 days 0.0 0.0 SHREVE 4257
2 483 days 0.0 0.0 NA
3 371 days 997824.9 890012.2 NA
4 291 days 0.0 0.0 CHURCH 7943
5 184 days 0.0 0.0 LABADIE 4446
6 124 days 0.0 0.0 TEXAS 3709
7 42 days 996991.1 894592.8 ITASKA 3111
8 31 days 0.0 0.0 SAINT LOUIS 4753
9 31 days 992687.3 890685.3 NA
10 29 days 1028778.0 888118.1 NA
11 27 days 1034026.0 898534.9 GREEN LEA 4136
12 27 days 1023634.0 907592.0 14TH 1908
13 27 days 1030898.0 901867.4 NA
14 27 days 1048605.0 896231.8 GIMBLIN 1020
15 27 days 1028286.0 893354.1 ALDINE 4349
16 27 days 1034113.0 885395.3 NA
17 27 days 1021943.0 899509.6 DELMAR 3114
18 27 days 1027916.0 905727.4 KNAPP 3245
19 27 days 1028080.0 894410.9 COTE BRILLIANTE 4229
20 27 days 1000447.0 877220.9 KINSEY 6272
21 27 days 1036161.0 882481.3 KENNERLY 6101
22 27 days 1033304.0 887147.7 NA
23 27 days 1029233.0 890945.9 NA
24 27 days 1034806.0 882698.1 NA
25 27 days 1029923.0 880264.4 HAMILTON 1021
26 27 days 1019440.0 905891.9 15TH 710
27 26 days 1032130.0 896900.4 NATURAL BRIDGE 4231
28 26 days 998275.6 894535.1 MINNESOTA 4457
29 26 days 1047905.0 897340.6 NA
30 26 days 0.0 0.0 NA
31 26 days 1004111.0 893520.9 GRAND 3630
32 26 days 1007221.0 884519.4 HEREFORD 3322
33 26 days 1003295.0 895121.2 NA
34 26 days 1030650.0 887444.4 DR MARTIN LUTHER KING 5100
35 26 days 995444.5 892927.1 VIRGINIA 5301
36 26 days 1032146.0 883637.7 NA
37 25 days 1034757.0 904588.8 BROADWAY 4828
38 25 days 1028137.0 893625.3 BILLUPS 1705
39 25 days 1053153.0 898033.6 NA
40 25 days 1031750.0 883103.6 MINERVA 5634
41 25 days 998801.4 894874.6 MICHIGAN 4414
42 25 days 1027393.0 904794.8 RAUSCHENBACH 3117
43 25 days 1039733.0 885195.7 MCARTHUR 5910
44 25 days 1025430.0 897494.3 CASS 3731
45 24 days 1051995.0 895308.4 RIVERVIEW 1124
46 24 days 1005529.0 890871.6 POTOMAC 3954
47 24 days 1000978.0 886650.3 WALLACE 4341
48 24 days 1034920.0 886010.0 BURD 2728
49 24 days 1033370.0 887518.1 NA
50 24 days 1028707.0 884870.6 NA
51 24 days 1031967.0 895056.4 NA
52 24 days 1022360.0 900798.4 NA
53 24 days 1046675.0 889809.7 MIMIKA 5531
54 23 days 1007382.0 893365.3 CONNECTICUT 3634
55 23 days 1036961.0 883830.0 SAINT LOUIS 5920
56 23 days 1042272.0 893522.1 NA
57 23 days 1014127.0 903976.8 DILLON 1124
58 23 days 1032130.0 896900.4 NATURAL BRIDGE 4231
59 23 days 1036605.0 897450.3 CARRIE 4531
60 23 days 1022548.0 907625.4 NA
61 23 days 1038155.0 895308.8 FLORISSANT 4700
62 23 days 1003278.0 895156.4 COMPTON 3720
63 23 days 1037063.0 886918.4 PALM 5550
64 23 days 1022299.0 906762.9 COCHRAN 1461
65 23 days 1026766.0 897107.0 GARFIELD 4000
66 23 days 1043213.0 892050.1 THRUSH 5400
67 23 days 1000926.0 892725.6 MERAMEC 3711
68 22 days 1047342.0 889848.5 FLOY 5594
69 22 days 1044996.0 892586.6 NA
70 22 days 988016.4 886631.2 DAVIS 547
71 22 days 1046716.0 896542.6 SWITZER 951
72 22 days 0.0 0.0 NA
73 22 days 1029418.0 891030.9 4949
74 22 days 1002487.0 898351.1 NA
75 22 days 1030139.0 904277.8 NA
76 22 days 1034151.0 902558.0 NA
77 22 days 1048015.0 896324.3 FREDERICK 8216
78 22 days 1021607.0 898042.0 NA
79 22 days 1034699.0 884938.9 HIGHLAND 5627
80 21 days 1052128.0 895835.8 NA
81 21 days 1044850.0 892558.1 FLORISSANT 5728
82 21 days 1048122.0 897219.8 BROADWAY 8220
83 21 days 1004800.0 894538.1 NA
84 21 days 1035792.0 892505.1 NA
85 21 days 1042225.0 890033.2 PLOVER 4936
86 21 days 1034573.0 896427.9 NEWSTEAD 4140
87 21 days 1026560.0 887259.6 ENRIGHT 5048
88 20 days 1031415.0 894194.6 NEWSTEAD 2931
89 20 days 1028621.0 898253.1 NA
90 20 days 997279.0 894676.9 MICHIGAN 4626
91 20 days 1031778.0 890118.8 HIGHLAND 4900
92 20 days 1002564.0 891249.9 NA
93 20 days 1030531.0 898549.0 LEXINGTON 3900
94 20 days 1035777.0 893192.8 NA
95 20 days 1032928.0 897204.1 NA
96 20 days 1005006.0 892615.9 GILES 3521
97 19 days 1000824.0 894103.1 LOUISIANA 4114
98 19 days 1030386.0 901979.7 PECK 4012
99 19 days 1025417.0 892611.9 PENDLETON 926
100 19 days 1013903.0 904245.2 NA
101 19 days 1025707.0 896608.4 NA
102 19 days 1044225.0 891667.2 ROBIN 5434
103 19 days 1038048.0 900410.6 GRAND 1325
104 19 days 1026655.0 891146.6 NA
105 19 days 1001651.0 899383.5 WISCONSIN 3853
106 19 days 1030024.0 900011.5 NATURAL BRIDGE 3836
107 19 days 997345.6 894541.8 MICHIGAN 4600
108 19 days 1044387.0 887882.4 NA
109 19 days 1022813.0 905078.3 MURPHY PARK 1851
110 19 days 1026907.0 899641.4 NA
111 18 days 1030515.0 885139.5 MINERVA 5363
112 18 days 998619.1 894877.5 MICHIGAN 4340
113 18 days 1000689.0 896718.0 NA
114 18 days 1000348.0 894739.9 MERAMEC 3147
115 17 days 995729.4 891902.7 IDAHO 5417
116 17 days 990205.4 888646.9 GRAND 1325
117 17 days 1028346.0 893245.7 ALDINE 4349
118 17 days 1030457.0 898946.1 PALM 3921
119 17 days 1021285.0 887849.5 PARKVIEW 4921
120 17 days 1027106.0 887992.3 KINGSHIGHWAY 900
121 17 days 1030385.0 891428.5 MARKET 4639
122 17 days 1026678.0 898056.9 NA
123 17 days 1005252.0 894945.9 VIRGINIA 3429
124 17 days 1027150.0 889813.4 MCMILLAN 4700
125 17 days 1032050.0 894045.3 LABADIE 4446
126 17 days 1028243.0 904858.9 NA
127 17 days 1044511.0 887701.6 GOODFELLOW 5003
128 17 days 1000773.0 894063.4 NA
129 16 days 1044511.0 887701.6 NA
130 16 days 1033378.0 891132.4 NA
131 16 days 1034206.0 903869.1 JOHN 1449
132 16 days 1048634.0 890708.3 NORTH POINTE 6145
133 16 days 1004299.0 891279.4 NA
134 16 days 1000305.0 887120.0 DELOR 5254
135 16 days 1025296.0 893849.6 C D BANKS 4155
136 16 days 1029258.0 906530.4 11TH 3505
137 15 days 1035297.0 895854.2 PENROSE 4481
138 15 days 1033212.0 884433.3 THEODOSIA 5601
139 15 days 1035636.0 893450.8 SHREVE 4049
140 15 days 1027261.0 896310.5 GARFIELD 4000
141 15 days 1021049.0 900148.2 SAMUEL SHEPARD 2946
142 14 days 1038855.0 892836.5 NA
143 14 days 1036218.0 884340.4 ROOSEVELT 5816
144 14 days 1002831.0 896061.4 CHIPPEWA 3116
145 14 days 0.0 0.0 TENNESSEE 5226
146 13 days 1035671.0 897440.9 CLARENCE 4401
147 13 days 1029887.0 889834.6 DR MARTIN LUTHER KING 4821
148 13 days 1035834.0 884252.7 NA
149 13 days 999847.4 885892.1 NA
150 13 days 0.0 0.0 4949
151 13 days 1036000.0 888884.8 UNION 3431
152 13 days 0.0 0.0 NA
153 13 days 1037099.0 895579.4 RICHARD 4625
154 13 days 998998.5 896676.4 NA
155 12 days 1031341.0 891609.6 MARCUS 2613
156 12 days 1044484.0 888564.8 NA
157 12 days 1030127.0 886695.8 RIDGE 5138
158 12 days 1017204.0 908242.7 NA
159 12 days 1032972.0 903934.3 19TH 4406
160 12 days 1029537.0 893621.6 NA
161 12 days 1025357.0 907701.4 NA
162 12 days 1033897.0 889429.4 ST LOUIS 5109
163 12 days 1022022.0 907595.8 13TH 1430
164 11 days 1017799.0 907619.9 9TH 205
165 11 days 1020329.0 899238.6 OLIVE 3037
166 11 days 1031357.0 905076.9 NA
167 11 days 997764.3 895610.9 NEBRASKA 4529
168 11 days 1026735.0 891120.7 NA
169 11 days 1007405.0 894425.6 NA
170 11 days 1035772.0 882305.7 WABADA 5969
171 11 days 1026184.0 890301.9 ENRIGHT 4550
172 11 days 1051786.0 894677.4 HOWELL 1181
173 11 days 1027321.0 888019.9 NA
174 11 days 1051778.0 894566.9 NA
175 11 days 1032520.0 880346.8 NA
176 11 days 1051838.0 895354.6 HOWELL 1115
177 10 days 1028775.0 900428.8 NA
178 10 days 1027075.0 896370.4 GARFIELD 4000
179 10 days 1022154.0 898673.6 NA
180 10 days 1004925.0 897760.9 CHEROKEE 2720
181 10 days 1026933.0 888149.1 AUBERT 773
182 10 days 1027279.0 891495.9 NA
183 10 days 1022136.0 905133.4 HOGAN 1800
184 10 days 998003.9 893209.4 VIRGINIA 4518
185 9 days 1010572.0 903703.8 ALLEN 1051
186 9 days 1025124.0 905021.8 BENTON 1933
187 9 days 1045249.0 887714.6 LALITE 6336
188 9 days 1037168.0 894947.6 NA
189 9 days 990701.9 888591.6 NA
190 9 days 1028552.0 881199.8 GOODFELLOW 853
191 9 days 993755.2 878508.2 GRAVOIS 7422
192 9 days 1046545.0 891148.8 NA
193 9 days 1068605.0 910845.4 EB 270 NA
194 9 days 1017616.0 876377.6 PARK 6763
195 9 days 1028716.0 890091.6 PAGE 4711
196 9 days 1013953.0 904086.3 NA
197 9 days 1031748.0 885691.9 DR MARTIN LUTHER KING 5378
198 9 days 1030986.0 885172.3 NA
199 9 days 1022057.0 901215.1 STODDARD 2800
200 8 days 1022134.0 909573.1 NA
201 8 days 1006156.0 896621.1 PENNSYLVANIA 3244
202 8 days 1030986.0 885172.3 NA
203 8 days 1040130.0 894837.9 NA
204 8 days 1040705.0 891528.7 ARLINGTON 4941
205 8 days 1036066.0 893029.3 KOSSUTH 4863
206 8 days 1024730.0 907940.6 NA
207 7 days 1018977.0 905530.4 NA
208 7 days 1030451.0 896704.6 WHITTIER 3047
209 7 days 1018061.0 895739.8 SCOTT 3560
210 7 days 1040680.0 890932.3 EMERSON 4921
211 7 days 1032299.0 886623.5 PATTON 5331
212 7 days 1034664.0 896014.3 LEE 4438
213 7 days 1033549.0 887857.3 UNION 2700
214 7 days 1026696.0 899404.5 MONTGOMERY 3461
215 7 days 1035119.0 883649.6 HIGHLAND 5807
216 6 days 1032339.0 900019.9 SHERMAN 3927
217 6 days 1027313.0 897869.5 COTTAGE 3834
218 6 days 1034257.0 901754.4 NA
219 6 days 1044313.0 891175.7 GILMORE 5276
220 6 days 1002248.0 887263.9 MORGANFORD 4522
221 6 days 0.0 0.0 NA
222 6 days 1007882.0 895751.5 HARTFORD 3230
223 6 days 1033378.0 891132.4 EUCLID 2944
224 6 days 1031231.0 903749.4 PENROSE 2106
225 6 days 1032953.0 887081.5 WABADA 5330
226 6 days 1032241.0 900648.8 LEE 3833
227 6 days 1035883.0 883594.1 MAFFITT 5800
228 6 days 1028851.0 891679.1 DR MARTIN LUTHER KING 4582
229 5 days 992870.4 892982.7 PENNSYLVANIA 5913
230 5 days 1032898.0 894410.2 NA
231 5 days 1028603.0 888564.7 AUBERT 1200
232 5 days 1051408.0 896489.1 CANAAN 907
233 5 days 1030932.0 893627.2 NA
234 5 days 1032937.0 892505.8 ASHLAND 4710
235 5 days 1026577.0 894318.1 EVANS 4200
236 5 days 1037449.0 894152.3 NA
237 5 days 1002881.0 895726.6 NA
238 5 days 1029269.0 898082.8 LABADIE 3945
239 5 days 1026084.0 899342.4 NA
240 5 days 1028563.0 898133.8 VANDEVENTER 2816
241 5 days 1043141.0 890387.4 WREN 5015
242 5 days 1017271.0 903906.1 NA
243 5 days 1022183.0 899505.3 NA
244 5 days 989533.2 887239.5 SCHIRMER 800
245 5 days 1002406.0 891194.5 ALBERTA 3921
246 5 days 989042.8 889513.5 PENNSYLVANIA 7403
247 5 days 997804.3 891252.8 DELOR 3659
248 5 days 1033277.0 904660.4 BISSELL 1121
249 5 days 1011331.0 881563.1 DALTON 0
250 5 days 1049783.0 891947.9 MORA 8561
251 5 days 1028461.0 885269.8 NA
252 5 days 1031326.0 890200.3 HAMMETT 4851
253 5 days 1049854.0 895810.5 HALLS FERRY 8612
254 5 days 1033725.0 882188.8 NA
255 5 days 1046626.0 890521.4 SHULTE 6035
256 4 days 1033658.0 899795.6 NA
257 4 days 1027078.0 888592.4 EUCLID 785
258 4 days 1031732.0 902337.5 GRAND 4206
259 4 days 990479.7 888796.9 NA
260 4 days 1051754.0 896324.3 ELIAS 933
261 4 days 1047754.0 889373.8 NA
262 4 days 1048493.0 896371.1 CHURCH 8309
263 4 days 1033060.0 891237.8 LABADIE 4843
264 4 days 1043363.0 890705.5 WREN 5055
265 4 days 1020209.0 903370.6 21ST 715
266 4 days 996624.4 891625.1 LOUISIANA 5211
267 3 days 1024271.0 906633.1 NA
268 3 days 1041538.0 892153.7 ALCOTT 5200
269 3 days 1027247.0 903401.7 DODIER NA
270 3 days 1022198.0 907641.5 14TH 1430
271 3 days 1035428.0 893986.3 4949
272 3 days 1024089.0 908269.7 NA
273 3 days 1033423.0 897476.8 KOSSUTH 4235
274 3 days 1034813.0 903047.6 DE SOTO 1409
275 3 days 995171.7 893913.2 WALSH 308
276 3 days 1002376.0 893358.7 NA
277 3 days 0.0 0.0 NA
278 3 days 1031432.0 891251.6 NA
279 3 days 990000.4 888462.2 NA
280 3 days 1003994.0 894935.3 VIRGINIA 3620
281 2 days 1015110.0 900117.9 RUTGER 2654
282 2 days 1051640.0 897259.3 ELIAS 835
283 2 days 1051479.0 895927.5 CANAAN 971
284 2 days 1040904.0 886178.6 NA
285 2 days 1035696.0 893689.0 KOSSUTH 4727
286 2 days 1016725.0 890017.6 NORFOLK 4250
287 2 days 1030720.0 896366.2 ASHLAND 4279
288 1 days 1010894.0 893626.0 SHENANDOAH 3658
289 1 days 1004511.0 898021.4 OHIO 3452
290 1 days 1004375.0 884540.4 CHIPPEWA 4939
291 1 days 1027553.0 893493.7 DR MARTIN LUTHER KING 4308
292 1 days 1001617.0 895896.4 PENNSYLVANIA 3942
293 1 days 1004029.0 888199.9 MORGANFORD 4254
294 1 days 1037577.0 885193.6 SELBER 5830
295 1 days 1043688.0 891512.5 WREN 5270
296 1 days 1003043.0 890577.3 DUNNICA 3946
297 1 days 1023898.0 906643.6 NA
298 1 days 1019893.0 910547.7 2ND 999
299 1 days 1049007.0 890159.1 GOODFELLOW 5961
300 1 days 1034124.0 895794.9 FARLIN 4447
301 1 days 1052972.0 896665.4 RIVERVIEW 911
302 0 days 1040599.0 893346.5 GERALDINE 5304
303 0 days 1035764.0 882107.0 WABADA 5962
304 0 days 1033922.0 888254.7 TERRY 5252
305 0 days 1036786.0 886544.7 CLARA 3340
306 0 days 1042473.0 892352.3 DAVISON 5271
307 0 days 995260.5 893647.8 EICHELBERGER 411
308 0 days 995670.8 891021.7 NA
309 0 days 1031520.0 885578.9 ARLINGTON 1460
310 0 days 1038456.0 900558.8 NA
311 -1 days 1031068.0 885126.1 ARLINGTON 1401
312 -1 days 1028747.0 898586.3 LABADIE 3800
313 -1 days 1010081.0 903858.6 NA
314 -1 days 1016086.0 908223.8 CLARK 601
315 -1 days 1032564.0 881482.7 NA
316 -1 days 1017361.0 890066.3 MANCHESTER 4238
317 -1 days 1035157.0 902483.6 CONDE 5220
318 -1 days 1036030.0 882441.9 HIGHLAND 5900
319 -1 days 1043154.0 890135.3 NA
320 -1 days 1036149.0 891990.5 FARLIN 4950
321 -1 days 1022299.0 906762.9 CASS 1415
322 -1 days 989055.9 889596.9 UPTON 225
323 -1 days 1000754.0 895093.4 MICHIGAN 4100
324 -2 days 997429.1 891964.9 TENNESSEE 4758
325 -2 days 1037384.0 885274.5 GOODFELLOW 5713
326 -2 days 1003002.0 895591.7 NA
327 -2 days 1029828.0 885989.0 MINERVA 5232
328 -2 days 1035240.0 898489.6 ATHLONE 4430
329 -2 days 1051865.0 896562.4 HARLAN 917
330 -2 days 1041712.0 893530.7 CLAXTON 5357
331 -2 days 1039648.0 885117.3 FERRIS 5911
332 -2 days 1028291.0 891706.2 NA
333 -2 days 1028254.0 890506.0 PAGE 4634
334 -2 days 1035149.0 882181.7 NA
335 -2 days 1013424.0 904108.4 NA
336 -3 days 1003486.0 896527.1 NEBRASKA 3646
337 -3 days 1032899.0 894125.9 LEXINGTON 4534
338 -3 days 1033526.0 899422.0 CLAY 4221
339 -3 days 1034569.0 886330.5 NA
340 NA days 0.0 0.0 EUCLID 1202
341 NA days 1008027.0 901258.3 CONGRESS 1909
342 NA days 1032246.0 900444.8 LEE 3856
343 NA days 1028281.0 894047.3 COTE BRILLIANTE 4200
344 NA days 1010480.0 895630.6 LOUISIANA 2328
345 NA days 992407.4 890121.1 IDAHO 6602
346 NA days 1034773.0 881698.6 NA
347 NA days 1031673.0 884752.1 BELT 1439
348 NA days 1032225.0 882995.8 BLACKSTONE 1387
349 NA days 1028866.0 877725.8 NA
350 NA days 1002881.0 895726.6 NA
351 NA days 1017651.0 909633.8 NA
352 NA days 1042247.0 890065.3 PLOVER 4938
353 NA days 1046874.0 889008.8 GOODFELLOW 5517
354 NA days 1030180.0 888420.4 KINGSHIGHWAY 1408
355 NA days 1008747.0 887198.5 LACKLAND 3139
356 NA days 1048551.0 893434.8 NA
357 NA days 1047618.0 897453.9 BITTNER 732
358 NA days 1028958.0 887970.3 MAPLE 5009
LocationComment LocationName
1 SAMS ST. LOUIS PACKING CO
2
3 REAR
4
5
6
7
8
9
10 AUTO ZONE
11
12
13 DOLLAR GENERAL
14
15
16
17
18
19
20
21 ON STREET ON STREET
22
23 NBC LOUNGE
24
25
26
27 WEST PARKING LOT M&A LIQUOR STORE
28 MOUNT PLEASANT PARK
29
30
31 PAPA JOHNS PIZZA
32 APARTMENT BUILDING
33 STREET
34 THE OTHER PLACE LOUNGE
35
36
37 HOTEL- FIRST WESTERN INN
38
39 RESIDENCE RESIDENCE
40
41
42
43
44
45
46
47 ALLEY ADJACENT TO SIDE WINDOW
48 LONDON S BOARDING HOUSE
49
50 REAR ALLEY
51
52
53
54
55
56
57 CLINTON-PEABODY HOUSING COMPLEX
58 M & A MARKET PARKING LOT
59 JUST INSIDE OF TREE LINE IN REAR OF
60
61 @AMEREN - 158
62
63
64
65
66
67 SECOND FLOOR
68
69
70
71
72
73
74
75
76 REAR YARD OF VACANT RESIDENCE
77
78
79
80
81
82
83 GRAVOIS PARK
84
85
86
87 REAR
88
89 MIDWEST PETROLEUM
90
91
92 SOUTH SIDE OF AMBERG PARK
93
94 APT A
95
96
97
98
99
100
101 CAR WASH AND AUTOMOBILE DETAILING BU BIG P. S CAR WASH AND DETAILING
102
103
104
105 APT B
106
107
108 ST. LOUIS FISH & CHICKEN
109 IN STREET MURPHY PARK APARTMENTS
110 UPPER LEVEL CLUB
111
112
113
114
115
116
117 STREET
118
119 14TH FLOOR
120
121
122
123
124
125
126
127
128
129 @BP GAS STATION PARKING LOT
130
131
132
133
134
135
136
137
138
139
140 VACANT LOT
141
142
143
144 IN STREET
145 VICTIM S RESPONDED TO THE 5200 BLOCK
146
147 BEST AUTO PLEX
148 GROCERY STORE PRICE CHOPPER
149 5401 MISSOURI ROUTE 30 LUCKY DUCK RESTAURANT
150
151 @SCHNUCKS - CITY PLAZA ON UNION
152
153 TWO-FAMILY APARTMENT BLDG
154 CONOCO GAS STATION
155 EAST SIDEWALK
156
157
158
159
160
161
162 NORTH ALLEY
163 CASS BANK
164
165 INTERIOR/SIDEWALK/PARKING LOT/STREET OLIVE BAR
166 WINDSOR PARK
167 STREET
168
169
170 VACANT RESIDENCE
171
172
173 CROWN FOOD MART
174
175 JULIAN AT HODIAMONT
176
177 BING LAU
178
179
180
181
182
183 REAR
184
185
186 APT A
187 REAR
188
189
190
191 APARTMENT COMPLEX GRAVOIS PLACE APARTMENTS
192 REAR ALLEY
193
194
195
196 ST. LOUIS HOUSING AUTHORITY CLINTON PEABODY PUBLIC HOUSING COMP
197
198
199 REAR
200
201
202
203
204
205
206
207
208
209
210
211
212 REAR GARAGE
213
214
215
216
217
218
219
220 THE ELLENWOOD BUILDING
221 FAIRGROUNDS PARK FAIRGROUNDS PARK
222 @ROOSEVELT HIGH SCHOOL
223
224 ALLEY
225
226
227
228
229
230
231
232
233 EAST SIDE OF STREET IN FRONT OF A VA
234
235
236
237
238 2ND FLOOR
239
240 MIDWEST PETROLEUM
241
242 STREET
243
244
245
246
247
248
249 DUPLEX ATTACHED TO 2641 DALTON
250
251 HAROLD S CHOP SUEY
252
253 APARTMENT RIVERVIEW APARTMENTS
254
255
256
257
258 @HOTEL- ECONOMY INN
259
260
261
262 ALLEY
263
264
265 APT 412
266
267 NUMERICAL ADDRESS OF THE PRIMARY SC
268 REAR ALLEY
269
270 @DOMINOS PIZZA - N 13TH
271
272
273 ALLEY/DUMPSTER
274 INTERSECTION OF E DE SOTO AVENUE AND
275 APT C
276
277
278
279 VACANT LOT
280
281 APARTMENT COMPLEX CAROLINE PLACE APARTMENTS
282
283
284
285 REAR ALLEY
286
287
288
289
290 APT 3E
291 PARKING LOT MARTIN LUTHER KING PLAZA
292
293
294
295
296
297
298 STREET
299
300
301
302
303
304
305
306
307 REAR SCHMIDT EQUIPMENT AND SUPPLY
308
309
310 LOVES TRUCK STOP
311 HANK S PACKAGE LIQUOR
312
313 @BAR-BASTILLE BASTILLE
314 BALL PARK VILLAGE BALLPARK VILLAGE / BUDWEISER BREW H
315
316
317
318
319
320
321 ON STREET
322 1ST FL
323
324
325 GOODFELLOW PLACE APARTMENTS
326
327
328
329
330
331
332
333
334
335 PARKING LOT CLINTON PEABODY
336 REAR EAST ALLEY
337
338
339 REAR
340
341
342
343 VACANT LOT
344 1ST FLOOR
345
346
347 STREET
348
349 VACANT LOT
350
351 MANSION HOUSE
352
353 REAR ALLEY
354
355
356
357
358 REAR DETACHED GARAGE- VACANT RESIDEN
Neighborhood ILEADSStreet ILEADSAddress Description
1 69 SHREVE 4257 HOMICIDE
2 62 9TH 1418 HOMICIDE
3 3 S 38TH ST 5215 HOMICIDE
4 74 CHURCH 7944 HOMICIDE
5 56 LABADIE 4446 HOMICIDE
6 19 TEXAS 3709 HOMICIDE
7 17 ITASKA ST 3111 HOMICIDE
8 56 SAINT LOUIS 4751 HOMICIDE
9 1 HOLLY HILLS AVE 629 HOMICIDE
10 53 N KINGSHIGHWAY BLVD 1225 HOMICIDE
11 68 W GREEN LEA PL 4132 HOMICIDE
12 63 N 14TH ST 1908 HOMICIDE
13 67 N GRAND BLVD 4038 HOMICIDE
14 74 GIMBLIN ST 1018 HOMICIDE
15 57 ALDINE AVE 4337 HOMICIDE
16 50 BURD AVE 2524 HOMICIDE
17 59 FRANKLIN AVE 3114 HOMICIDE
18 65 KNAPP ST 3245 HOMICIDE
19 57 W COTE BRILLIANTE AVE 4229 HOMICIDE
20 8 KINSEY PL 6272 HOMICIDE
21 50 KENNERLY AVE 5972 HOMICIDE
22 50 GROVER ST 2521 HOMICIDE
23 56 DR MARTIN LUTHER KING DR 4621 HOMICIDE
24 50 COTE BRILLIANTE AVE 5895 HOMICIDE
25 48 HAMILTON AVE 1021 HOMICIDE
26 36 N 15TH ST 709 HOMICIDE
27 68 NATURAL BRIDGE AVE 4231 HOMICIDE
28 17 MICHIGAN AVE 4461 HOMICIDE
29 74 BADEN AVE 724 HOMICIDE
30 39 I 64 WESTBOUND AT CLAYTON AVE 0 HOMICIDE
31 19 S GRAND BLVD 3630 HOMICIDE
32 14 HEREFORD ST 3322 HOMICIDE
33 19 S COMPTON AVE 3751 HOMICIDE
34 51 DR MARTIN LUTHER KING DR 5084 HOMICIDE
35 1 VIRGINIA AVE 5301 HOMICIDE
36 78 CLARA AVE 1401 HOMICIDE
37 64 N BROADWAY 4828 HOMICIDE
38 57 BILLUPS AVE 1707 HOMICIDE
39 74 LOWELL ST 8861 HOMICIDE
40 78 MINERVA AVE 5634 HOMICIDE
41 17 OSCEOLA ST 3110 HOMICIDE
42 60 RAUSCHENBACH AVE 3117 HOMICIDE
43 70 MCARTHUR AVE 5910 HOMICIDE
44 59 DR MARTIN LUTHER KING DR 3731 HOMICIDE
45 74 RIVERVIEW BLVD 1124 HOMICIDE
46 15 POTOMAC ST 3949 HOMICIDE
47 5 WALLACE AVE 4314 HOMICIDE
48 50 BURD AVE 2728 HOMICIDE
49 50 UNION BLVD 2611 HOMICIDE
50 49 VERNON AVE 5326 HOMICIDE
51 56 N NEWSTEAD AVE 3224 HOMICIDE
52 59 GAMBLE ST 2900 HOMICIDE
53 76 ERA AVE 5530 HOMICIDE
54 15 CONNECTICUT ST 3634 HOMICIDE
55 50 SAINT LOUIS AVE 5920 HOMICIDE
56 71 EMERSON AVE 5416 HOMICIDE
57 33 DILLON DR 1124 HOMICIDE
58 68 NATURAL BRIDGE AVE 4231 HOMICIDE
59 69 CARRIE AVE 4539 HOMICIDE
60 60 N 13TH ST 1513 HOMICIDE
61 71 SHREVE AVE 5318 HOMICIDE
62 19 S COMPTON AVE 3720 HOMICIDE
63 50 PALM ST 5556 HOMICIDE
64 61 COCHRAN PL 1459 HOMICIDE
65 56 GARFIELD AVE 3915 HOMICIDE
66 72 THRUSH AVE 5400 HOMICIDE
67 16 MERAMEC ST 3711 HOMICIDE
68 76 FLOY AVE 5592 HOMICIDE
69 72 ROBIN AVE 5598 HOMICIDE
70 2 W DAVIS ST 547 HOMICIDE
71 74 SWITZER AVE 951 HOMICIDE
72 67 FAIRGROUNDS PL 4100 HOMICIDE
73 56 DICK GREGORY PL 1524 HOMICIDE
74 18 CHIPPEWA ST 2605 HOMICIDE
75 65 N 21ST ST 3915 HOMICIDE
76 66 E PRAIRIE AVE 2009 HOMICIDE
77 74 FREDERICK ST 8216 HOMICIDE
78 77 JOSEPHINE BAKER AVE 700 HOMICIDE
79 50 HIGHLAND AVE 5627 HOMICIDE
80 74 RIVERVIEW BLVD 1052 HOMICIDE
81 72 W FLORISSANT AVE 5728 HOMICIDE
82 74 N BROADWAY 8216 HOMICIDE
83 19 LOUISIANA AVE 3513 HOMICIDE
84 69 FARLIN AVE 4892 HOMICIDE
85 72 PLOVER AVE 4936 HOMICIDE
86 69 N NEWSTEAD AVE 4149 HOMICIDE
87 51 ENRIGHT AVE 5048 HOMICIDE
88 56 LABADIE AVE 4435 HOMICIDE
89 59 N VANDEVENTER 2815 HOMICIDE
90 17 MINNESOTA AVE 4629 HOMICIDE
91 55 HIGHLAND AVE 4900 HOMICIDE
92 16 KEOKUK ST 3836 HOMICIDE
93 56 LEXINGTON AVE 3963 HOMICIDE
94 69 KOSSUTH AVE 4834 HOMICIDE
95 68 RED BUD AVE 3980 HOMICIDE
96 15 GILES AVE 3521 HOMICIDE
97 16 LOUISIANA AVE 4114 HOMICIDE
98 67 BAILEY AVE 3215 HOMICIDE
99 58 PENDLETON AVE 924 HOMICIDE
100 33 HICKORY LN 1435 HOMICIDE
101 77 N VANDEVENTER AVE 1420 HOMICIDE
102 72 ROBIN AVE 5428 HOMICIDE
103 79 E CARRIE AVE / I 70 WESTBOUND 0 HOMICIDE
104 54 MCMILLAN AVE 4561 HOMICIDE
105 18 WISCONSIN AVE 3853 HOMICIDE
106 59 PRAIRIE AVE 3614 HOMICIDE
107 17 MICHIGAN AVE 4622 HOMICIDE
108 76 GOODFELLOW BLVD 5000 HOMICIDE
109 61 MURPHY PARK DR 1847 HOMICIDE
110 59 N GRAND BLVD 2546 HOMICIDE
111 78 MINERVA AVE 5363 HOMICIDE
112 17 MICHIGAN AVE 4404 HOMICIDE
113 16 CALIFORNIA AVE 4056 HOMICIDE
114 16 MERAMEC ST 3145 HOMICIDE
115 1 IDAHO AVE 5417 HOMICIDE
116 2 VERMONT AVE 7315 HOMICIDE
117 57 ALDINE AVE 4349 HOMICIDE
118 56 PALM ST 3926 HOMICIDE
119 38 PARKVIEW PL 4921 HOMICIDE
120 53 N KINGSHIGHWAY BLVD 900 HOMICIDE
121 56 N MARKET ST 4647 HOMICIDE
122 59 N MARKET ST 3800 HOMICIDE
123 30 VIRGINIA AVE 3429 HOMICIDE
124 54 MCMILLAN AVE 4735 HOMICIDE
125 56 N TAYLOR AVE 3120 HOMICIDE
126 65 N FLORISSANT AVE 3330 HOMICIDE
127 76 GOODFELLOW BLVD 5003 HOMICIDE
128 16 KLOCKE ST 3400 HOMICIDE
129 76 GOODFELLOW BLVD 5003 HOMICIDE
130 55 N EUCLID AVE 2944 HOMICIDE
131 66 E JOHN AVE 1439 HOMICIDE
132 73 NORTH POINTE BLVD 6139 HOMICIDE
133 15 GUSTINE AVE 3619 HOMICIDE
134 5 DELOR ST 4254 HOMICIDE
135 58 C D BANKS AVE 4158 HOMICIDE
136 65 N 14TH ST 3504 HOMICIDE
137 69 PENROSE ST 4481 HOMICIDE
138 50 THEODOSIA AVE 5606 HOMICIDE
139 69 SHREVE AVE 4049 HOMICIDE
140 56 GARFIELD AVE 4040 HOMICIDE
141 37 SAMUEL SHEPARD DR 2946 HOMICIDE
142 84 I 70 WESTBOUND / N KINGSHIGHWA 0 HOMICIDE
143 50 ROOSEVELT PL 5816 HOMICIDE
144 16 CHIPPEWA ST 3013 HOMICIDE
145 0 UNKNOWN 0 HOMICIDE
146 68 CLARENCE AVE 4415 HOMICIDE
147 55 DR MARTIN LUTHER KING DR 4815 HOMICIDE
148 50 GOODFELLOW BLVD 2747 HOMICIDE
149 5 GRAVOIS AVE 5401 HOMICIDE
150 0 UNKNOWN 0 HOMICIDE
151 50 UNION BLVD 3431 HOMICIDE
152 72 W FLORISSANT AVE 5500 HOMICIDE
153 69 RICHARD PL 4625 HOMICIDE
154 17 S BROADWAY 4355 HOMICIDE
155 55 MARCUS AVE 2613 HOMICIDE
156 76 SHERRY AVE 6120 HOMICIDE
157 51 RIDGE AVE 5140 HOMICIDE
158 35 CHESTNUT ST 714 HOMICIDE
159 66 N 19TH ST 4406 HOMICIDE
160 57 SAINT FERDINAND AVE 4370 HOMICIDE
161 63 N MARKET PL 1101 HOMICIDE
162 52 ST LOUIS AVE 5105 HOMICIDE
163 62 N 13TH ST 1420 HOMICIDE
164 35 N 9TH ST 205 HOMICIDE
165 37 OLIVE ST 3037 HOMICIDE
166 65 BLAIR AVE 4109 HOMICIDE
167 17 NEBRASKA AVE 4528 HOMICIDE
168 54 MCMILLAN AVE 4503 HOMICIDE
169 25 ARKANSAS AVE 3170 HOMICIDE
170 50 WABADA AVE 5969 HOMICIDE
171 54 ENRIGHT AVE 4550 HOMICIDE
172 74 HOWELL ST 1181 HOMICIDE
173 53 N KINGSHIGHWAY BLVD 930 HOMICIDE
174 74 HALLS FERRY RD 9006 HOMICIDE
175 48 JULIAN AVE 5985 HOMICIDE
176 74 HOWELL ST 1115 HOMICIDE
177 59 N GRAND BLVD 3101 HOMICIDE
178 56 GARFIELD AVE 4012 HOMICIDE
179 77 FRANKLIN AVE 3311 HOMICIDE
180 19 IOWA AVE 3420 HOMICIDE
181 53 AUBERT AVE 773 HOMICIDE
182 54 NEWBERRY TER 4502 HOMICIDE
183 61 HOGAN ST 1320 HOMICIDE
184 16 ALASKA AVE 4630 HOMICIDE
185 21 ALLEN AVE 1051 HOMICIDE
186 60 BENTON ST 1933 HOMICIDE
187 76 SHERRY AVE 6341 HOMICIDE
188 69 SEXAUER AVE 4420 HOMICIDE
189 1 IDAHO AVE 7138 HOMICIDE
190 48 GOODFELLOW BLVD 853 HOMICIDE
191 4 GRAVOIS AVE 7422 HOMICIDE
192 76 SUMMIT AVE 5629 HOMICIDE
193 75 RIVERVIEW DR / WB 270 0 HOMICIDE
194 44 W PARK AVE 6763 HOMICIDE
195 54 PAGE BLVD 4711 HOMICIDE
196 33 HICKORY LN 1468 HOMICIDE
197 78 DR MARTIN LUTHER KING DR 5390 HOMICIDE
198 78 RIDGE AVE 5368 HOMICIDE
199 59 STODDARD ST 2800 HOMICIDE
200 63 N 7TH ST / I 70 WESTBOUND 0 HOMICIDE
201 30 PENNSYLVANIA AVE 3244 HOMICIDE
202 78 RIDGE AVE 5401 HOMICIDE
203 71 N KINGSHIGHWAY BLVD 5406 HOMICIDE
204 71 ARLINGTON AVE 4943 HOMICIDE
205 69 KOSSUTH AVE 4863 HOMICIDE
206 63 CLINTON ST 1200 HOMICIDE
207 36 LOCUST ST 1527 HOMICIDE
208 56 WHITTIER ST 3047 HOMICIDE
209 37 S GRAND BLVD 715 HOMICIDE
210 72 EMERSON AVE 4921 HOMICIDE
211 50 PATTON AVE 5331 HOMICIDE
212 69 LEE AVE 4440 HOMICIDE
213 52 UNION BLVD 2700 HOMICIDE
214 59 MONTGOMERY ST 3561 HOMICIDE
215 50 HIGHLAND AVE 5824 HOMICIDE
216 67 SHERMAN PL 3921 HOMICIDE
217 59 COTTAGE AVE 3842 HOMICIDE
218 66 E LINTON AVE 2157 HOMICIDE
219 72 GILMORE AVE 5276 HOMICIDE
220 5 MORGANFORD RD 4528 HOMICIDE
221 83 FAIRGROUNDS PARK DR 3900 HOMICIDE
222 25 HARTFORD ST 3230 HOMICIDE
223 55 N EUCLID AVE 2944 HOMICIDE
224 65 PENROSE ST 2115 HOMICIDE
225 50 WABADA AVE 5324 HOMICIDE
226 67 LEE AVE 3838 HOMICIDE
227 50 MAFFITT AVE 5870 HOMICIDE
228 56 DR MARTIN LUTHER KING DR 4557 HOMICIDE
229 1 PENNSYLVANIA AVE 5913 HOMICIDE
230 56 LEXINGTON AVE 4476 HOMICIDE
231 53 AUBERT AVE 1224 HOMICIDE
232 74 CANAAN AVE 915 HOMICIDE
233 57 N TAYLOR AVE 2814 HOMICIDE
234 55 MARCUS AVE 3061 HOMICIDE
235 58 EVANS AVE 4220 HOMICIDE
236 69 CARTER AVE 4836 HOMICIDE
237 16 MINNESOTA AVE 3754 HOMICIDE
238 56 LABADIE AVE 3945 HOMICIDE
239 59 BACON ST 2409 HOMICIDE
240 56 N VANDEVENTER AVE 2821 HOMICIDE
241 72 WREN AVE 5019 HOMICIDE
242 36 S 18TH ST / CLARK AVE 0 HOMICIDE
243 59 BELL AVE 3100 HOMICIDE
244 1 SCHIRMER ST 802 HOMICIDE
245 16 ALBERTA ST 3921 HOMICIDE
246 2 PENNSYLVANIA AVE 7403 HOMICIDE
247 16 DELOR ST 3659 HOMICIDE
248 66 RANDALL PL 4426 HOMICIDE
249 13 DALTON AVE 2639 HOMICIDE
250 73 MORA LN 8561 HOMICIDE
251 51 UNION BLVD 1122 HOMICIDE
252 55 HAMMETT PL 4851 HOMICIDE
253 74 HALLS FERRY RD 8612 HOMICIDE
254 78 HAMILTON AVE 1452 HOMICIDE
255 76 SHULTE AVE 6035 HOMICIDE
256 68 CARTER AVE 4042 HOMICIDE
257 53 N EUCLID AVE 785 HOMICIDE
258 67 N GRAND BLVD 4206 HOMICIDE
259 1 ALABAMA AVE 7146 HOMICIDE
260 74 ELIAS AVE 933 HOMICIDE
261 76 ACME AVE 5636 HOMICIDE
262 74 CHURCH RD 8309 HOMICIDE
263 55 LABADIE AVE 4843 HOMICIDE
264 72 WREN AVE 5055 HOMICIDE
265 36 N 21ST ST 715 HOMICIDE
266 1 LOUISIANA AVE 5205 HOMICIDE
267 63 CLINTON ST 1455 HOMICIDE
268 72 EMERSON AVE 5201 HOMICIDE
269 60 E DODIER ST 2511 HOMICIDE
270 62 N 13TH ST 1430 HOMICIDE
271 69 MARCUS AVE 4100 HOMICIDE
272 63 CHAMBERS ST 1120 HOMICIDE
273 68 E KOSSUTH AVE 4235 HOMICIDE
274 66 E DE SOTO AVE 1401 HOMICIDE
275 17 WALSH ST 308 HOMICIDE
276 16 S GRAND BLVD 3900 HOMICIDE
277 0 UNKNOWN 0 HOMICIDE
278 55 CUPPLES PL 4741 HOMICIDE
279 2 VERMONT AVE 7343 HOMICIDE
280 19 VIRGINIA AVE 3620 HOMICIDE
281 31 HICKORY ST 2651 HOMICIDE
282 74 ELIAS AVE 827 HOMICIDE
283 74 CANAAN AVE 971 HOMICIDE
284 70 GOODFELLOW BLVD 4301 HOMICIDE
285 69 SHREVE AVE 4106 HOMICIDE
286 39 NORFOLK AVE 4247 HOMICIDE
287 56 NEW ASHLAND PL 3100 HOMICIDE
288 27 SHENANDOAH AVE 3658 HOMICIDE
289 19 OHIO AVE 3454 HOMICIDE
290 14 CHIPPEWA ST 4939 HOMICIDE
291 58 DR MARTIN LUTHER KING DR 4308 HOMICIDE
292 16 PENNSYLVANIA AVE 3942 HOMICIDE
293 15 MERAMEC ST 4255 HOMICIDE
294 50 GOODFELLOW BLVD 3351 HOMICIDE
295 72 WREN AVE 5270 HOMICIDE
296 16 DUNNICA AVE 3946 HOMICIDE
297 60 MADISON ST 1501 HOMICIDE
298 35 CARR ST 202 HOMICIDE
299 73 GOODFELLOW BLVD 5961 HOMICIDE
300 69 FARLIN AVE 4435 HOMICIDE
301 74 RIVERVIEW BLVD 907 HOMICIDE
302 71 GERALDINE AVE 5300 HOMICIDE
303 50 WABADA AVE 5962 HOMICIDE
304 52 TERRY AVE 5252 HOMICIDE
305 50 CLARA AVE 3340 HOMICIDE
306 72 DAVISON AVE 5271 HOMICIDE
307 17 EICHELBERGER ST 411 HOMICIDE
308 1 BATES ST 1005 HOMICIDE
309 78 ARLINGTON AVE 1460 HOMICIDE
310 79 N BROADWAY 6124 HOMICIDE
311 78 ARLINGTON AVE 1401 HOMICIDE
312 59 LABADIE AVE 3850 HOMICIDE
313 21 RUSSELL AVE 1027 HOMICIDE
314 35 CLARK AVE 601 HOMICIDE
315 78 HAMILTON TER 5946 HOMICIDE
316 39 MANCHESTER AVE 4229 HOMICIDE
317 66 CONDE ST 5220 HOMICIDE
318 50 HIGHLAND AVE 5971 HOMICIDE
319 72 ROBIN AVE 5016 HOMICIDE
320 69 FARLIN AVE 4950 HOMICIDE
321 61 COCHRAN PL 1457 HOMICIDE
322 2 UPTON ST 225 HOMICIDE
323 16 MICHIGAN AVE 4108 HOMICIDE
324 16 TENNESSEE AVE 4758 HOMICIDE
325 50 GOODFELLOW PL 5713 HOMICIDE
326 19 MINNESOTA AVE 3741 HOMICIDE
327 51 MINERVA AVE 5228 HOMICIDE
328 68 ATHLONE AVE 4448 HOMICIDE
329 74 HARLAN AVE 916 HOMICIDE
330 71 CLAXTON AVE 5381 HOMICIDE
331 70 FERRIS AVE 5911 HOMICIDE
332 54 EVANS AVE 4527 HOMICIDE
333 54 PAGE BLVD 4630 HOMICIDE
334 50 COTE BRILLIANTE AVE 5951 HOMICIDE
335 33 RUTGER LN 1417 HOMICIDE
336 19 NEBRASKA AVE 3646 HOMICIDE
337 56 LEXINGTON AVE 4520 HOMICIDE
338 68 CLAY AVE 4221 HOMICIDE
339 50 BELT AVE 2738 HOMICIDE
340 53 EUCLID 1202 HOMICIDE
341 22 CONGRESS ST 1909 HOMICIDE
342 67 LEE AVE 3836 HOMICIDE
343 57 W COTE BRILLIANTE AVE 4267 HOMICIDE
344 25 LOUISIANA AVE 2328 HOMICIDE
345 1 IDAHO AVE 6440 HOMICIDE
346 50 DR MARTIN LUTHER KING DR 5971 HOMICIDE
347 78 BELT AVE 1431 HOMICIDE
348 78 BLACKSTONE AVE 1387 HOMICIDE
349 48 N SKINKER BLVD 882 HOMICIDE
350 16 MINNESOTA AVE 3754 HOMICIDE
351 35 N 4TH ST 300 HOMICIDE
352 72 PLOVER AVE 4938 HOMICIDE
353 76 EMMA AVE 6307 HOMICIDE
354 53 N KINGSHIGHWAY BLVD 1408 HOMICIDE
355 15 LACKLAND AVE 3144 HOMICIDE
356 74 ETON LN 1545 HOMICIDE
357 74 BITTNER ST 732 HOMICIDE
358 51 MAPLE AVE 5009 HOMICIDE
District Crime DateOccur CodedMonth Complaint
1 6 10000 2016-06-09 08:28:00 2018-06-28 16-027833
2 4 10000 2017-09-01 13:44:00 2018-12-28 17-043306
3 1 10000 2017-02-22 23:45:00 2018-02-28 18-008475
4 6 10000 2018-06-10 00:01:00 2019-03-28 18-026258
5 6 10000 2018-05-28 00:01:00 2018-11-28 18-023972
6 3 10000 2018-07-27 20:15:00 2018-11-28 18-034672
7 1 10000 2018-03-17 23:00:00 2018-04-28 18-012176
8 5 10000 2018-03-28 18:15:00 2018-04-28 18-013752
9 1 10000 2019-06-27 01:48:00 2019-07-28 19-030335
10 5 10000 2019-10-30 21:07:00 2019-11-28 19-054615
11 6 10000 2018-12-01 17:37:00 2018-12-28 18-056958
12 4 10000 2018-11-01 23:00:00 2018-11-28 18-052272
13 4 10000 2018-11-01 13:07:00 2018-11-28 18-052152
14 6 10000 2018-08-01 23:03:00 2018-08-28 18-035664
15 5 10000 2018-05-01 21:20:00 2018-05-28 18-019411
16 5 10000 2018-05-01 12:47:00 2018-05-28 18-019311
17 4 10000 2018-05-01 11:00:00 2018-05-28 18-020625
18 4 10000 2018-03-01 18:30:00 2018-03-28 18-009700
19 5 10000 2018-03-01 08:50:00 2018-03-28 18-009431
20 2 10000 2018-02-01 16:30:00 2018-02-28 18-005350
21 5 10000 2018-01-01 17:00:00 2018-01-28 18-000142
22 5 10000 2019-04-01 15:29:00 2019-04-28 19-014217
23 5 10000 2019-06-01 01:03:00 2019-06-28 19-025297
24 5 10000 2019-06-01 02:48:00 2019-06-28 19-025312
25 5 10000 2019-09-01 04:15:00 2019-09-28 19-043120
26 4 10000 2019-11-01 02:22:00 2019-11-28 19-054833
27 6 10000 2018-12-02 15:16:00 2018-12-28 18-057082
28 1 10000 2018-08-02 14:02:00 2018-08-28 18-035796
29 6 10000 2018-07-02 22:00:00 2018-07-28 18-030399
30 2 10000 2018-06-02 14:37:00 2018-06-28 18-024858
31 3 10000 2018-06-02 01:34:00 2018-06-28 18-024776
32 2 10000 2018-05-02 19:00:00 2018-05-28 18-019792
33 3 10000 2018-05-02 15:01:00 2018-05-28 18-019544
34 5 10000 2018-02-02 23:25:00 2018-02-28 18-005382
35 1 10000 2019-06-02 08:52:00 2019-06-28 19-025531
36 5 10000 2019-06-02 10:05:00 2019-06-28 19-025525
37 6 10000 2018-12-03 01:24:00 2018-12-28 18-057161
38 5 10000 2018-07-03 17:34:00 2018-07-28 18-030526
39 6 10000 2018-04-03 18:00:00 2018-04-28 18-014712
40 5 10000 2019-01-03 13:36:00 2019-01-28 19-000429
41 1 10000 2019-02-03 19:30:00 2019-02-28 19-005180
42 4 10000 2019-06-03 06:03:00 2019-06-28 19-025632
43 6 10000 2019-07-03 08:31:00 2019-07-28 19-031517
44 4 10000 2019-10-03 15:02:00 2019-10-28 19-049602
45 6 10000 2018-11-04 21:05:00 2018-11-28 18-052745
46 2 10000 2018-07-04 23:45:00 2018-07-28 18-030719
47 1 10000 2018-06-04 21:43:00 2018-06-28 18-025251
48 5 10000 2019-03-04 20:03:00 2019-03-28 19-009857
49 5 10000 2019-05-04 03:13:00 2019-05-28 19-020139
50 5 10000 2019-05-04 10:40:00 2019-05-28 19-020183
51 6 10000 2019-06-04 01:40:00 2019-06-28 19-025830
52 4 10000 2019-07-04 21:55:00 2019-07-28 19-031835
53 6 10000 2019-10-04 23:45:00 2019-10-28 19-049868
54 2 10000 2018-12-05 16:45:00 2018-12-28 18-057634
55 5 10000 2018-09-05 08:18:00 2018-09-28 18-041936
56 6 10000 2018-08-05 21:10:00 2018-08-28 18-036365
57 3 10000 2018-07-05 21:26:00 2018-07-28 18-030894
58 6 10000 2018-07-05 19:44:00 2018-07-28 18-030877
59 6 10000 2018-07-05 11:18:00 2018-07-28 18-030812
60 4 10000 2018-07-05 00:20:00 2018-07-28 18-030729
61 6 10000 2018-03-05 00:45:00 2018-03-28 18-009971
62 3 10000 2018-01-05 21:45:00 2018-01-28 18-000847
63 5 10000 2019-03-05 13:00:00 2019-03-28 19-009999
64 4 10000 2019-08-05 01:18:00 2019-08-28 19-037801
65 5 10000 2019-09-05 00:51:00 2019-09-28 19-043868
66 6 10000 2019-09-05 13:25:00 2019-09-28 19-043969
67 1 10000 2019-11-05 09:26:00 2019-11-28 19-055608
68 6 10000 2018-09-06 19:09:00 2018-09-28 18-042231
69 6 10000 2018-07-06 21:42:00 2018-07-28 18-031078
70 1 10000 2018-06-06 05:40:00 2018-06-28 18-025503
71 6 10000 2018-05-06 16:25:00 2018-05-28 18-020241
72 6 10000 2018-05-06 13:22:00 2018-05-28 18-020208
73 5 10000 2018-04-06 20:10:00 2018-04-28 18-015241
74 3 10000 2019-01-06 15:30:00 2019-01-28 19-000915
75 4 10000 2019-04-06 15:35:00 2019-04-28 19-015072
76 6 10000 2019-04-06 21:50:00 2019-04-28 19-015262
77 6 10000 2019-05-06 23:15:00 2019-05-28 19-020657
78 4 10000 2019-06-06 11:58:00 2019-06-28 19-026300
79 5 10000 2019-11-06 23:00:00 2019-11-28 19-055927
80 6 10000 2018-08-07 22:30:00 2018-08-28 18-036817
81 6 10000 2018-06-07 04:04:00 2018-06-28 18-025671
82 6 10000 2019-01-07 22:09:00 2019-01-28 19-001140
83 3 10000 2019-07-07 01:45:00 2019-07-28 19-032275
84 6 10000 2019-07-07 13:56:00 2019-07-28 19-032327
85 6 10000 2019-08-07 16:26:00 2019-08-28 19-038332
86 6 10000 2019-09-07 00:45:00 2019-09-28 19-044294
87 5 10000 2019-10-07 10:01:00 2019-10-28 19-050231
88 6 10000 2018-09-08 20:39:00 2018-09-28 18-042567
89 4 10000 2018-08-08 22:35:00 2018-08-28 18-037014
90 1 10000 2018-07-08 19:20:00 2018-07-28 18-031390
91 5 10000 2019-02-08 11:55:00 2019-02-28 19-005986
92 1 10000 2019-04-08 18:30:00 2019-04-28 19-015480
93 6 10000 2019-05-08 21:28:00 2019-05-28 19-021058
94 6 10000 2019-05-08 21:35:00 2019-05-28 19-021064
95 6 10000 2019-06-08 22:30:00 2019-06-28 19-026743
96 2 10000 2019-10-08 21:23:00 2019-10-28 19-050574
97 3 10000 2018-12-09 00:01:00 2018-12-28 18-058563
98 4 10000 2018-07-09 21:12:00 2018-07-28 18-031594
99 5 10000 2018-03-09 15:00:00 2018-03-28 18-010716
100 3 10000 2018-02-09 21:09:00 2018-02-28 18-006392
101 4 10000 2018-01-09 17:31:00 2018-01-28 18-001431
102 6 10000 2019-01-09 00:10:00 2019-01-28 19-001348
103 6 10000 2019-01-09 22:00:00 2019-01-28 19-001524
104 5 10000 2019-04-09 17:58:00 2019-04-28 19-015697
105 3 10000 2019-04-09 22:45:00 2019-04-28 19-015726
106 4 10000 2019-06-09 15:56:00 2019-06-28 19-026862
107 1 10000 2019-06-09 20:32:00 2019-06-28 19-026905
108 6 10000 2019-08-09 13:54:00 2019-08-28 19-038644
109 4 10000 2019-08-09 22:30:00 2019-08-28 19-038720
110 4 10000 2019-11-09 01:50:00 2019-11-28 19-056263
111 5 10000 2018-09-10 21:49:00 2018-09-28 18-042941
112 1 10000 2018-05-10 20:10:00 2018-05-28 18-020963
113 3 10000 2018-03-10 01:04:00 2018-03-28 18-010757
114 3 10000 2019-07-10 11:28:00 2019-07-28 19-032846
115 1 10000 2018-09-11 22:20:00 2018-09-28 18-043140
116 1 10000 2018-07-11 17:40:00 2018-07-28 18-031935
117 5 10000 2018-06-11 14:41:00 2018-06-28 18-026460
118 6 10000 2018-05-11 16:47:00 2018-05-28 18-021101
119 5 10000 2018-01-11 10:28:00 2018-01-28 18-001737
120 5 10000 2019-02-11 16:00:00 2019-02-28 19-007995
121 5 10000 2019-02-11 20:50:00 2019-02-28 19-006513
122 4 10000 2019-02-11 22:00:00 2019-02-28 19-006512
123 3 10000 2019-04-11 19:40:00 2019-04-28 19-016075
124 5 10000 2019-07-11 00:42:00 2019-07-28 19-032961
125 6 10000 2019-07-11 18:00:00 2019-07-28 19-033107
126 4 10000 2019-08-11 02:00:00 2019-08-28 19-038947
127 6 10000 2019-09-11 23:27:00 2019-09-28 19-045265
128 3 10000 2019-11-11 15:54:00 2019-11-28 19-056681
129 6 10000 2018-12-12 12:53:00 2018-12-28 18-058750
130 6 10000 2018-05-12 21:15:00 2018-05-28 18-021274
131 6 10000 2018-02-12 19:39:00 2018-02-28 18-006829
132 6 10000 2018-01-12 22:47:00 2018-01-28 18-001995
133 2 10000 2018-01-12 10:00:00 2018-01-28 18-001895
134 1 10000 2019-01-12 22:22:00 2019-01-28 19-001896
135 5 10000 2019-03-12 00:45:00 2019-03-28 19-010953
136 4 10000 2019-08-12 17:06:00 2019-08-28 19-039284
137 6 10000 2018-08-13 23:55:00 2018-08-28 18-037897
138 5 10000 2018-06-13 22:34:00 2018-06-28 18-026927
139 6 10000 2019-05-13 20:52:00 2019-05-28 19-021920
140 5 10000 2019-09-13 18:43:00 2019-09-28 19-045605
141 4 10000 2019-10-13 18:46:00 2019-10-28 19-051389
142 6 10000 2018-12-14 03:46:00 2018-12-28 18-058996
143 5 10000 2018-08-14 09:15:00 2018-08-28 18-037957
144 3 10000 2019-06-14 23:50:00 2019-06-28 19-027970
145 0 10000 2019-09-14 03:50:00 2019-09-28 19-045679
146 6 10000 2018-11-15 19:18:00 2018-11-28 18-054501
147 5 10000 2018-09-15 06:30:00 2018-09-28 18-044019
148 5 10000 2018-07-15 13:32:00 2018-07-28 18-032477
149 1 10000 2018-04-15 01:11:00 2018-04-28 18-016585
150 0 10000 2019-01-15 13:24:00 2019-01-28 19-002283
151 5 10000 2019-01-15 15:12:00 2019-01-28 19-002278
152 6 10000 2019-06-15 09:37:00 2019-06-28 19-028025
153 6 10000 2019-06-15 11:10:00 2019-06-28 19-028033
154 1 10000 2019-07-15 02:54:00 2019-07-28 19-033672
155 5 10000 2018-12-16 07:38:00 2018-12-28 18-059333
156 6 10000 2018-09-16 16:10:00 2018-09-28 18-044013
157 5 10000 2018-08-16 04:00:00 2018-08-28 18-038296
158 4 10000 2018-06-16 12:34:00 2018-06-28 18-027359
159 4 10000 2018-06-16 04:30:00 2018-06-28 18-027307
160 5 10000 2019-01-16 23:48:00 2019-01-28 19-002489
161 4 10000 2019-08-16 17:13:00 2019-08-28 19-040096
162 5 10000 2019-08-16 22:21:00 2019-08-28 19-040121
163 4 10000 2019-10-16 21:01:00 2019-10-28 19-052008
164 4 10000 2018-08-17 10:48:00 2018-08-28 18-038533
165 4 10000 2018-06-17 23:55:00 2018-06-28 18-027605
166 4 10000 2018-06-17 00:05:00 2018-06-28 18-027449
167 1 10000 2018-03-17 23:19:00 2018-03-28 18-012030
168 5 10000 2018-03-17 16:10:00 2018-03-28 18-011972
169 3 10000 2018-01-17 23:45:00 2018-01-28 18-002737
170 5 10000 2019-01-17 16:31:00 2019-01-28 19-002622
171 5 10000 2019-03-17 21:40:00 2019-03-28 19-011872
172 6 10000 2019-06-17 03:10:00 2019-06-28 19-028344
173 5 10000 2019-07-17 00:06:00 2019-07-28 19-034095
174 6 10000 2019-07-17 23:00:00 2019-07-28 19-034271
175 5 10000 2019-07-17 23:10:00 2019-07-28 19-034276
176 6 10000 2019-10-17 01:16:00 2019-10-28 19-052025
177 4 10000 2018-08-18 12:58:00 2018-08-28 18-038739
178 5 10000 2018-03-18 09:19:00 2018-03-28 18-012080
179 4 10000 2018-01-18 13:41:00 2018-01-28 18-002826
180 3 10000 2019-03-18 21:08:00 2019-03-28 19-012060
181 5 10000 2019-03-18 23:39:00 2019-03-28 19-012105
182 5 10000 2019-07-18 10:10:00 2019-07-28 19-034345
183 4 10000 2019-08-18 02:15:00 2019-08-28 19-040288
184 1 10000 2019-11-18 00:50:00 2019-11-28 19-057833
185 3 10000 2018-12-19 23:35:00 2018-12-28 18-059926
186 4 10000 2018-12-19 23:30:00 2018-12-28 18-059924
187 6 10000 2018-07-19 18:30:00 2018-07-28 18-033260
188 6 10000 2018-05-19 21:45:00 2018-05-28 18-022532
189 1 10000 2018-05-19 02:40:00 2018-05-28 18-022391
190 5 10000 2018-03-19 10:26:00 2018-03-28 18-012231
191 1 10000 2018-01-19 17:25:00 2018-01-28 18-003034
192 6 10000 2019-03-19 11:07:00 2019-03-28 19-012127
193 6 10000 2019-05-19 20:06:00 2019-05-28 19-023050
194 2 10000 2019-07-19 12:50:00 2019-07-28 19-034583
195 5 10000 2019-07-19 20:31:00 2019-07-28 19-034640
196 3 10000 2019-09-19 20:51:00 2019-09-28 19-046892
197 5 10000 2019-11-19 07:08:00 2019-11-28 19-058060
198 5 10000 2019-11-19 10:41:00 2019-11-28 19-058099
199 4 10000 2019-11-19 19:34:00 2019-11-28 19-058200
200 4 10000 2018-12-20 23:39:00 2018-12-28 18-060116
201 3 10000 2018-07-20 05:02:00 2018-07-28 18-033301
202 5 10000 2018-03-20 19:37:00 2018-03-28 18-012492
203 6 10000 2018-01-20 22:53:00 2018-01-28 18-003218
204 6 10000 2019-05-20 21:38:00 2019-05-28 19-023244
205 6 10000 2019-07-20 15:00:00 2019-07-28 19-034765
206 4 10000 2019-10-20 05:10:00 2019-10-28 19-052582
207 4 10000 2018-11-21 15:24:00 2018-11-28 18-055391
208 6 10000 2018-09-21 09:53:00 2018-09-28 18-044906
209 4 10000 2018-08-21 18:38:00 2018-08-28 18-039325
210 6 10000 2019-01-21 07:00:00 2019-01-28 19-003132
211 5 10000 2019-06-21 13:55:00 2019-06-28 19-029263
212 6 10000 2019-06-21 17:00:00 2019-06-28 19-029441
213 5 10000 2019-07-21 05:09:00 2019-07-28 19-034864
214 4 10000 2019-07-21 12:59:00 2019-07-28 19-034925
215 5 10000 2019-09-21 11:13:00 2019-09-28 19-047170
216 6 10000 2018-12-22 19:35:00 2018-12-28 18-060407
217 4 10000 2018-07-22 19:34:00 2018-07-28 18-033754
218 6 10000 2018-05-22 13:12:00 2018-05-28 18-022949
219 6 10000 2018-04-22 06:00:00 2018-04-28 18-017730
220 1 10000 2018-02-22 23:21:00 2018-02-28 18-008449
221 6 10000 2019-05-22 18:16:00 2019-05-28 19-023607
222 3 10000 2019-05-22 23:00:00 2019-05-28 19-023675
223 6 10000 2019-06-22 18:32:00 2019-06-28 19-029491
224 4 10000 2019-08-22 07:36:00 2019-08-28 19-041089
225 5 10000 2019-09-22 03:35:00 2019-09-28 19-047288
226 6 10000 2019-09-22 14:51:00 2019-09-28 19-047376
227 5 10000 2019-09-22 23:31:00 2019-09-28 19-047468
228 5 10000 2019-11-22 10:35:00 2019-11-28 19-058671
229 1 10000 2018-12-23 16:45:00 2018-12-28 18-060518
230 6 10000 2018-12-23 12:32:00 2018-12-28 18-060494
231 5 10000 2018-12-23 04:00:00 2018-12-28 18-060452
232 6 10000 2018-09-23 20:38:00 2018-09-28 18-045308
233 5 10000 2018-09-23 12:23:00 2018-09-28 18-045238
234 6 10000 2018-09-23 11:47:00 2018-09-28 18-045235
235 5 10000 2018-09-23 07:22:00 2018-09-28 18-045200
236 6 10000 2018-08-23 10:15:00 2018-08-28 18-039664
237 3 10000 2018-07-23 17:30:00 2018-07-28 18-033958
238 6 10000 2018-07-23 03:01:00 2018-07-28 18-033795
239 4 10000 2018-07-23 00:40:00 2018-07-28 18-033782
240 5 10000 2018-03-23 20:10:00 2018-03-28 18-012955
241 6 10000 2018-01-23 22:40:00 2018-01-28 18-003685
242 4 10000 2018-01-23 13:29:00 2018-01-28 18-003611
243 4 10000 2019-01-23 07:48:00 2019-01-28 19-003372
244 1 10000 2019-01-23 21:12:00 2019-01-28 19-003512
245 1 10000 2019-01-23 22:56:00 2019-01-28 19-003515
246 1 10000 2019-04-23 13:00:00 2019-04-28 19-018137
247 1 10000 2019-06-23 00:20:00 2019-06-28 19-029522
248 4 10000 2019-06-23 15:00:00 2019-06-28 19-029774
249 2 10000 2019-07-23 21:00:00 2019-07-28 19-035553
250 6 10000 2019-07-23 21:55:00 2019-07-28 19-035400
251 5 10000 2019-08-23 20:06:00 2019-08-28 19-041462
252 5 10000 2019-09-23 19:40:00 2019-09-28 19-047675
253 6 10000 2019-09-23 20:45:00 2019-09-28 19-047678
254 5 10000 2019-09-23 21:49:00 2019-09-28 19-047681
255 6 10000 2019-10-23 20:30:00 2019-10-28 19-053304
256 6 10000 2018-12-24 12:22:00 2018-12-28 18-060607
257 5 10000 2018-09-24 04:00:00 2018-09-28 18-045348
258 4 10000 2018-04-24 03:14:00 2018-04-28 18-018038
259 1 10000 2019-03-24 21:15:00 2019-03-28 19-012978
260 6 10000 2019-04-24 03:08:00 2019-04-28 19-018259
261 6 10000 2019-05-24 21:47:00 2019-05-28 19-024031
262 6 10000 2019-06-24 12:00:00 2019-06-28 19-030268
263 6 10000 2019-06-24 22:58:00 2019-06-28 19-029879
264 6 10000 2019-08-24 14:25:00 2019-08-28 19-041580
265 4 10000 2019-08-24 14:46:00 2019-08-28 19-041611
266 1 10000 2019-11-24 23:23:00 2019-11-28 19-059088
267 4 10000 2018-09-25 22:03:00 2018-09-28 18-045713
268 6 10000 2018-09-25 12:29:00 2018-09-28 18-045634
269 4 10000 2018-06-25 21:36:00 2018-06-28 18-029092
270 4 10000 2018-06-25 17:15:00 2018-06-28 18-029066
271 6 10000 2018-06-25 10:39:00 2018-06-28 18-029029
272 4 10000 2018-05-25 01:15:00 2018-05-28 18-023389
273 6 10000 2018-01-25 10:00:00 2018-01-28 18-004168
274 6 10000 2019-01-25 22:07:00 2019-01-28 19-003800
275 1 10000 2019-03-25 23:32:00 2019-03-28 19-013127
276 3 10000 2019-04-25 05:00:00 2019-04-28 19-018478
277 0 10000 2019-05-25 09:20:00 2019-05-28 19-024111
278 5 10000 2019-06-25 03:13:00 2019-06-28 19-029897
279 1 10000 2019-08-25 05:56:00 2019-08-28 19-041690
280 3 10000 2019-11-25 18:38:00 2019-11-28 19-059224
281 3 10000 2018-12-26 21:56:00 2018-12-28 18-060906
282 6 10000 2018-09-26 19:35:00 2018-09-28 18-045926
283 6 10000 2018-09-26 10:30:00 2018-09-28 18-045789
284 6 10000 2018-04-26 11:45:00 2018-04-28 18-018445
285 6 10000 2018-02-26 00:25:00 2018-02-28 18-008902
286 2 10000 2019-09-26 19:02:00 2019-09-28 19-048273
287 6 10000 2019-11-26 10:32:00 2019-11-28 19-059309
288 2 10000 2018-11-27 22:00:00 2018-11-28 18-056435
289 3 10000 2018-07-27 22:36:00 2018-07-28 18-034695
290 2 10000 2018-04-27 23:00:00 2018-04-28 18-018711
291 5 10000 2018-02-27 16:27:00 2018-02-28 18-009211
292 3 10000 2018-01-27 14:12:00 2018-01-28 18-004367
293 2 10000 2018-01-27 13:00:00 2018-01-28 18-004387
294 5 10000 2018-01-27 01:22:00 2018-01-28 18-004265
295 6 10000 2019-01-27 15:01:00 2019-01-28 19-004046
296 1 10000 2019-01-27 21:00:00 2019-01-28 19-004099
297 4 10000 2019-04-27 05:17:00 2019-04-28 19-018891
298 4 10000 2019-05-27 01:08:00 2019-05-28 19-024375
299 6 10000 2019-05-27 23:15:00 2019-05-28 19-024517
300 6 10000 2019-09-27 18:50:00 2019-09-28 19-048489
301 6 10000 2019-11-27 16:00:00 2019-11-28 19-059586
302 6 10000 2018-12-28 01:15:00 2018-12-28 18-061082
303 5 10000 2018-09-28 11:30:00 2018-09-28 18-046177
304 5 10000 2018-07-28 22:15:00 2018-07-28 18-034867
305 5 10000 2018-04-28 03:20:00 2018-04-28 18-018735
306 6 10000 2019-01-28 12:40:00 2019-01-28 19-004172
307 1 10000 2019-09-28 16:20:00 2019-09-28 19-048627
308 1 10000 2019-10-28 14:51:00 2019-10-28 19-054188
309 5 10000 2019-11-28 00:25:00 2019-11-28 19-059621
310 6 10000 2019-11-28 13:09:00 2019-11-28 19-059669
311 5 10000 2018-11-29 20:49:00 2018-11-28 18-056624
312 4 10000 2018-07-29 00:56:00 2018-07-28 18-034886
313 3 10000 2018-05-29 00:11:00 2018-05-28 18-023978
314 4 10000 2018-04-29 20:00:00 2018-04-28 18-018976
315 5 10000 2018-04-29 17:00:00 2018-04-28 18-018954
316 2 10000 2018-01-29 00:55:00 2018-01-28 18-004573
317 6 10000 2019-04-29 07:05:00 2019-04-28 19-019206
318 5 10000 2019-04-29 16:10:00 2019-04-28 19-019314
319 6 10000 2019-05-29 06:10:00 2019-05-28 19-024763
320 6 10000 2019-05-29 12:00:00 2019-05-28 19-025192
321 4 10000 2019-05-29 12:17:00 2019-05-28 19-024821
322 1 10000 2019-06-29 21:00:00 2019-06-28 19-030913
323 3 10000 2019-08-29 11:58:00 2019-08-28 19-042610
324 1 10000 2018-09-30 20:27:00 2018-09-28 18-046560
325 5 10000 2018-09-30 15:33:00 2018-09-28 18-046536
326 3 10000 2018-09-30 12:09:00 2018-09-28 18-046508
327 5 10000 2018-07-30 20:48:00 2018-07-28 18-035236
328 6 10000 2018-07-30 05:20:00 2018-07-28 18-035065
329 6 10000 2018-06-30 18:44:00 2018-06-28 18-029981
330 6 10000 2018-01-30 10:40:00 2018-01-28 18-004812
331 6 10000 2019-04-30 23:45:00 2019-04-28 19-019547
332 5 10000 2019-05-30 12:47:00 2019-05-28 19-025009
333 5 10000 2019-05-30 20:56:00 2019-05-28 19-025076
334 5 10000 2019-05-30 21:58:00 2019-05-28 19-025102
335 3 10000 2019-07-30 00:01:00 2019-07-28 19-036579
336 3 10000 2018-05-31 01:30:00 2018-05-28 18-024433
337 6 10000 2018-01-31 11:15:00 2018-01-28 18-004969
338 6 10000 2019-05-31 12:59:00 2019-05-28 19-025205
339 5 10000 2019-08-31 04:34:00 2019-08-28 19-042948
340 5 10000 <NA> 2018-10-28 18-019567
341 3 10000 <NA> 2018-10-28 18-046912
342 6 10000 <NA> 2018-10-28 18-046933
343 5 10000 <NA> 2018-10-28 18-046944
344 3 10000 <NA> 2018-10-28 18-047230
345 1 10000 <NA> 2018-10-28 18-047573
346 5 10000 <NA> 2018-10-28 18-047961
347 5 10000 <NA> 2018-10-28 18-047980
348 5 10000 <NA> 2018-10-28 18-048002
349 5 10000 <NA> 2018-10-28 18-048475
350 3 10000 <NA> 2018-10-28 18-048273
351 4 10000 <NA> 2018-10-28 18-048718
352 6 10000 <NA> 2018-10-28 18-048841
353 6 10000 <NA> 2018-10-28 18-049140
354 5 10000 <NA> 2018-10-28 18-049640
355 2 10000 <NA> 2018-10-28 18-051495
356 6 10000 <NA> 2018-10-28 18-051536
357 6 10000 <NA> 2018-10-28 18-051586
358 5 10000 <NA> 2018-10-28 18-051996
OGR data source with driver: ESRI Shapefile
Source: "E:\NEW-stl-louis-crime\St Louis Shape files\nbrhds_wards\BND_Nhd88_cw.shp", layer: "BND_Nhd88_cw"
with 88 features
It has 6 fields
Integer64 fields read as strings: NHD_NUM
Collected US Census data to bring in geospatial polygons that represent St Louis Neighborhoods.
Transformed mapview data into WGS84 structure.
Check to make sure data is a geospatial object.
Use census geospatial data to generate a map.
Observations: 88
Variables: 6
$ NHD_NUM <fct> 43, 29, 28, 40, 41, 42, 39, 44, 36, 37, 62, 61, 45, 77, ...
$ NHD_NAME <fct> Franz Park, Tiffany, Botanical Heights, Kings Oak, Chelt...
$ ANGLE <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,...
$ NHD_NUMTXT <fct> 43 Franz Park, 29 Tiffany, 28 Botanical Heights, 40 King...
$ SHAPE_area <dbl> 11012014, 5887342, 11586012, 4706723, 9245751, 9771242, ...
$ SHAPE_len <dbl> 14740.430, 10467.847, 14700.023, 9239.956, 12357.106, 12...
We have 88 neighborhoods and their name and number are factor types in R.
The polygon shapes are included in this data frame.
# A tibble: 23 x 3
# Groups: CodedMonth [23]
CodedMonth Crime n
<date> <int> <int>
1 2018-07-28 10000 23
2 2019-06-28 10000 23
3 2018-09-28 10000 21
4 2019-05-28 10000 21
5 2019-07-28 10000 21
6 2018-01-28 10000 19
7 2018-10-28 10000 19
8 2018-12-28 10000 19
9 2019-01-28 10000 18
10 2019-09-28 10000 18
# ... with 13 more rows
Group data by coded month.
Count the number of homicides per month.
Data presented in a bar graph with totals displayed above the bar.
I added a smoothing line to get a better view of the crime movement.
Note that October 2018 was the peak.
It was when Channel 5 reported the sever increase in carjackings. Looks like homicids too.
It was also the timeframe when they reported establishing atask force.
***
# A tibble: 61 x 6
NHD_NAME Crime n cumulative total cumul.percent
<fct> <int> <int> <int> <int> <dbl>
1 Wells Goodfellow 10000 26 26 358 7.26
2 Baden 10000 22 48 358 13.4
3 Greater Ville 10000 20 68 358 19.0
4 Dutchtown 10000 17 85 358 23.7
5 Penrose 10000 16 101 358 28.2
6 Walnut Park East 10000 15 116 358 32.4
7 Jeff Vanderlou 10000 14 130 358 36.3
8 Hamilton Heights 10000 12 142 358 39.7
9 Walnut Park West 10000 11 153 358 42.7
10 Carondelet 10000 10 163 358 45.5
# ... with 51 more rows
Had to adjust the factor variables (NHD_NAME) and to account for missing variables (NA).
Count by crime and put in decending order.
This is a display of the highest crime neighborhoods.
70% of the homicides are committed in the top 21 neighborhoods (23%)
***
Group by Neighborhood Name.
Chart puts data in a descending order and presents greater than 5.
***
Looks like there is a significant difference in times that the crime occured.
Seems to be more afternoon to midnight occurrances than post midnight to noon.
May need a further breakdown of crime times to make this meaningful.
Reporting.diff YCoord XCoord CADStreet
Length:358 Min. : 0 Min. : 0 Length:358
Class :difftime 1st Qu.:1010653 1st Qu.:888296 Class :character
Mode :numeric Median :1028815 Median :893443 Mode :character
Mean : 985648 Mean :858888
3rd Qu.:1035155 3rd Qu.:897453
Max. :1068605 Max. :910845
CADAddress LocationComment LocationName Neighborhood
Min. : 0 Length:358 Length:358 Length:358
1st Qu.:2845 Class :character Class :character Class :character
Median :4225 Mode :character Mode :character Mode :character
Mean :3868
3rd Qu.:5014
Max. :8612
NA's :112
ILEADSStreet ILEADSAddress Description District
Length:358 Min. : 0 Length:358 Min. :0.000
Class :character 1st Qu.:2200 Class :character 1st Qu.:3.000
Mode :character Median :3946 Mode :character Median :5.000
Mean :3675 Mean :4.372
3rd Qu.:4948 3rd Qu.:6.000
Max. :9006 Max. :6.000
Crime DateOccur CodedMonth
Min. :10000 Min. :2016-06-09 08:28:00 Min. :2018-01-28
1st Qu.:10000 1st Qu.:2018-07-01 20:22:00 1st Qu.:2018-07-28
Median :10000 Median :2019-01-07 22:09:00 Median :2018-12-28
Mean :10000 Mean :2018-12-23 20:31:13 Mean :2019-01-06
3rd Qu.:10000 3rd Qu.:2019-06-24 17:29:00 3rd Qu.:2019-06-28
Max. :10000 Max. :2019-11-28 13:09:00 Max. :2019-11-28
NA's :19
Complaint NHD_NAME
Length:358 Wells Goodfellow: 26
Class :character Baden : 22
Mode :character Greater Ville : 20
Dutchtown : 17
Penrose : 16
(Other) :254
NA's : 3
We will use the data we restructed earlier in the analysis.
We will use the crime D file.
Check the structure of the file we selected.
XCoord and YCoord coordinates are based on the State Plane North American Datum 1983 (NAD83) format.
This data will have to be converted to lat/long values.
Some of the XCoords and YCoords have values of O. This will need to be accounted for later in the analysis.
Reporting.diff YCoord XCoord CADStreet CADAddress
1 749 days 0 0 SHREVE 4257
2 483 days 0 0 NA
3 291 days 0 0 CHURCH 7943
4 184 days 0 0 LABADIE 4446
5 124 days 0 0 TEXAS 3709
6 31 days 0 0 SAINT LOUIS 4753
7 26 days 0 0 NA
8 22 days 0 0 NA
9 14 days 0 0 TENNESSEE 5226
10 13 days 0 0 4949
11 13 days 0 0 NA
12 6 days 0 0 NA
13 3 days 0 0 NA
14 NA days 0 0 EUCLID 1202
LocationComment LocationName Neighborhood
1 SAMS ST. LOUIS PACKING CO 69
2 62
3 74
4 56
5 19
6 56
7 39
8 67
9 VICTIM S RESPONDED TO THE 5200 BLOCK 0
10 0
11 72
12 FAIRGROUNDS PARK FAIRGROUNDS PARK 83
13 0
14 53
ILEADSStreet ILEADSAddress Description District Crime
1 SHREVE 4257 HOMICIDE 6 10000
2 9TH 1418 HOMICIDE 4 10000
3 CHURCH 7944 HOMICIDE 6 10000
4 LABADIE 4446 HOMICIDE 6 10000
5 TEXAS 3709 HOMICIDE 3 10000
6 SAINT LOUIS 4751 HOMICIDE 5 10000
7 I 64 WESTBOUND AT CLAYTON AVE 0 HOMICIDE 2 10000
8 FAIRGROUNDS PL 4100 HOMICIDE 6 10000
9 UNKNOWN 0 HOMICIDE 0 10000
10 UNKNOWN 0 HOMICIDE 0 10000
11 W FLORISSANT AVE 5500 HOMICIDE 6 10000
12 FAIRGROUNDS PARK DR 3900 HOMICIDE 6 10000
13 UNKNOWN 0 HOMICIDE 0 10000
14 EUCLID 1202 HOMICIDE 5 10000
DateOccur CodedMonth Complaint NHD_NAME
1 2016-06-09 08:28:00 2018-06-28 16-027833 Penrose
2 2017-09-01 13:44:00 2018-12-28 17-043306 Columbus Square
3 2018-06-10 00:01:00 2019-03-28 18-026258 Baden
4 2018-05-28 00:01:00 2018-11-28 18-023972 Greater Ville
5 2018-07-27 20:15:00 2018-11-28 18-034672 Gravois Park
6 2018-03-28 18:15:00 2018-04-28 18-013752 Greater Ville
7 2018-06-02 14:37:00 2018-06-28 18-024858 Forest Park South East
8 2018-05-06 13:22:00 2018-05-28 18-020208 Fairground Neighborhood
9 2019-09-14 03:50:00 2019-09-28 19-045679 <NA>
10 2019-01-15 13:24:00 2019-01-28 19-002283 <NA>
11 2019-06-15 09:37:00 2019-06-28 19-028025 Walnut Park East
12 2019-05-22 18:16:00 2019-05-28 19-023607 Fairground Park
13 2019-05-25 09:20:00 2019-05-28 19-024111 <NA>
14 <NA> 2018-10-28 18-019567 Fountain Park
Collect those records whose X/Y values are zeros.
These records will need a different type of processing.
Reporting.diff YCoord XCoord CADStreet CADAddress
1 371 days 997824.9 890012.2 NA
2 42 days 996991.1 894592.8 ITASKA 3111
3 31 days 992687.3 890685.3 NA
4 29 days 1028778.0 888118.1 NA
5 27 days 1034026.0 898534.9 GREEN LEA 4136
6 27 days 1023634.0 907592.0 14TH 1908
7 27 days 1030898.0 901867.4 NA
8 27 days 1048605.0 896231.8 GIMBLIN 1020
9 27 days 1028286.0 893354.1 ALDINE 4349
10 27 days 1034113.0 885395.3 NA
11 27 days 1021943.0 899509.6 DELMAR 3114
12 27 days 1027916.0 905727.4 KNAPP 3245
13 27 days 1028080.0 894410.9 COTE BRILLIANTE 4229
14 27 days 1000447.0 877220.9 KINSEY 6272
15 27 days 1036161.0 882481.3 KENNERLY 6101
16 27 days 1033304.0 887147.7 NA
17 27 days 1029233.0 890945.9 NA
18 27 days 1034806.0 882698.1 NA
19 27 days 1029923.0 880264.4 HAMILTON 1021
20 27 days 1019440.0 905891.9 15TH 710
21 26 days 1032130.0 896900.4 NATURAL BRIDGE 4231
22 26 days 998275.6 894535.1 MINNESOTA 4457
23 26 days 1047905.0 897340.6 NA
24 26 days 1004111.0 893520.9 GRAND 3630
25 26 days 1007221.0 884519.4 HEREFORD 3322
26 26 days 1003295.0 895121.2 NA
27 26 days 1030650.0 887444.4 DR MARTIN LUTHER KING 5100
28 26 days 995444.5 892927.1 VIRGINIA 5301
29 26 days 1032146.0 883637.7 NA
30 25 days 1034757.0 904588.8 BROADWAY 4828
31 25 days 1028137.0 893625.3 BILLUPS 1705
32 25 days 1053153.0 898033.6 NA
33 25 days 1031750.0 883103.6 MINERVA 5634
34 25 days 998801.4 894874.6 MICHIGAN 4414
35 25 days 1027393.0 904794.8 RAUSCHENBACH 3117
36 25 days 1039733.0 885195.7 MCARTHUR 5910
37 25 days 1025430.0 897494.3 CASS 3731
38 24 days 1051995.0 895308.4 RIVERVIEW 1124
39 24 days 1005529.0 890871.6 POTOMAC 3954
40 24 days 1000978.0 886650.3 WALLACE 4341
41 24 days 1034920.0 886010.0 BURD 2728
42 24 days 1033370.0 887518.1 NA
43 24 days 1028707.0 884870.6 NA
44 24 days 1031967.0 895056.4 NA
45 24 days 1022360.0 900798.4 NA
46 24 days 1046675.0 889809.7 MIMIKA 5531
47 23 days 1007382.0 893365.3 CONNECTICUT 3634
48 23 days 1036961.0 883830.0 SAINT LOUIS 5920
49 23 days 1042272.0 893522.1 NA
50 23 days 1014127.0 903976.8 DILLON 1124
51 23 days 1032130.0 896900.4 NATURAL BRIDGE 4231
52 23 days 1036605.0 897450.3 CARRIE 4531
53 23 days 1022548.0 907625.4 NA
54 23 days 1038155.0 895308.8 FLORISSANT 4700
55 23 days 1003278.0 895156.4 COMPTON 3720
56 23 days 1037063.0 886918.4 PALM 5550
57 23 days 1022299.0 906762.9 COCHRAN 1461
58 23 days 1026766.0 897107.0 GARFIELD 4000
59 23 days 1043213.0 892050.1 THRUSH 5400
60 23 days 1000926.0 892725.6 MERAMEC 3711
61 22 days 1047342.0 889848.5 FLOY 5594
62 22 days 1044996.0 892586.6 NA
63 22 days 988016.4 886631.2 DAVIS 547
64 22 days 1046716.0 896542.6 SWITZER 951
65 22 days 1029418.0 891030.9 4949
66 22 days 1002487.0 898351.1 NA
67 22 days 1030139.0 904277.8 NA
68 22 days 1034151.0 902558.0 NA
69 22 days 1048015.0 896324.3 FREDERICK 8216
70 22 days 1021607.0 898042.0 NA
71 22 days 1034699.0 884938.9 HIGHLAND 5627
72 21 days 1052128.0 895835.8 NA
73 21 days 1044850.0 892558.1 FLORISSANT 5728
74 21 days 1048122.0 897219.8 BROADWAY 8220
75 21 days 1004800.0 894538.1 NA
76 21 days 1035792.0 892505.1 NA
77 21 days 1042225.0 890033.2 PLOVER 4936
78 21 days 1034573.0 896427.9 NEWSTEAD 4140
79 21 days 1026560.0 887259.6 ENRIGHT 5048
80 20 days 1031415.0 894194.6 NEWSTEAD 2931
81 20 days 1028621.0 898253.1 NA
82 20 days 997279.0 894676.9 MICHIGAN 4626
83 20 days 1031778.0 890118.8 HIGHLAND 4900
84 20 days 1002564.0 891249.9 NA
85 20 days 1030531.0 898549.0 LEXINGTON 3900
86 20 days 1035777.0 893192.8 NA
87 20 days 1032928.0 897204.1 NA
88 20 days 1005006.0 892615.9 GILES 3521
89 19 days 1000824.0 894103.1 LOUISIANA 4114
90 19 days 1030386.0 901979.7 PECK 4012
91 19 days 1025417.0 892611.9 PENDLETON 926
92 19 days 1013903.0 904245.2 NA
93 19 days 1025707.0 896608.4 NA
94 19 days 1044225.0 891667.2 ROBIN 5434
95 19 days 1038048.0 900410.6 GRAND 1325
96 19 days 1026655.0 891146.6 NA
97 19 days 1001651.0 899383.5 WISCONSIN 3853
98 19 days 1030024.0 900011.5 NATURAL BRIDGE 3836
99 19 days 997345.6 894541.8 MICHIGAN 4600
100 19 days 1044387.0 887882.4 NA
101 19 days 1022813.0 905078.3 MURPHY PARK 1851
102 19 days 1026907.0 899641.4 NA
103 18 days 1030515.0 885139.5 MINERVA 5363
104 18 days 998619.1 894877.5 MICHIGAN 4340
105 18 days 1000689.0 896718.0 NA
106 18 days 1000348.0 894739.9 MERAMEC 3147
107 17 days 995729.4 891902.7 IDAHO 5417
108 17 days 990205.4 888646.9 GRAND 1325
109 17 days 1028346.0 893245.7 ALDINE 4349
110 17 days 1030457.0 898946.1 PALM 3921
111 17 days 1021285.0 887849.5 PARKVIEW 4921
112 17 days 1027106.0 887992.3 KINGSHIGHWAY 900
113 17 days 1030385.0 891428.5 MARKET 4639
114 17 days 1026678.0 898056.9 NA
115 17 days 1005252.0 894945.9 VIRGINIA 3429
116 17 days 1027150.0 889813.4 MCMILLAN 4700
117 17 days 1032050.0 894045.3 LABADIE 4446
118 17 days 1028243.0 904858.9 NA
119 17 days 1044511.0 887701.6 GOODFELLOW 5003
120 17 days 1000773.0 894063.4 NA
121 16 days 1044511.0 887701.6 NA
122 16 days 1033378.0 891132.4 NA
123 16 days 1034206.0 903869.1 JOHN 1449
124 16 days 1048634.0 890708.3 NORTH POINTE 6145
125 16 days 1004299.0 891279.4 NA
126 16 days 1000305.0 887120.0 DELOR 5254
127 16 days 1025296.0 893849.6 C D BANKS 4155
128 16 days 1029258.0 906530.4 11TH 3505
129 15 days 1035297.0 895854.2 PENROSE 4481
130 15 days 1033212.0 884433.3 THEODOSIA 5601
131 15 days 1035636.0 893450.8 SHREVE 4049
132 15 days 1027261.0 896310.5 GARFIELD 4000
133 15 days 1021049.0 900148.2 SAMUEL SHEPARD 2946
134 14 days 1038855.0 892836.5 NA
135 14 days 1036218.0 884340.4 ROOSEVELT 5816
136 14 days 1002831.0 896061.4 CHIPPEWA 3116
137 13 days 1035671.0 897440.9 CLARENCE 4401
138 13 days 1029887.0 889834.6 DR MARTIN LUTHER KING 4821
139 13 days 1035834.0 884252.7 NA
140 13 days 999847.4 885892.1 NA
141 13 days 1036000.0 888884.8 UNION 3431
142 13 days 1037099.0 895579.4 RICHARD 4625
143 13 days 998998.5 896676.4 NA
144 12 days 1031341.0 891609.6 MARCUS 2613
145 12 days 1044484.0 888564.8 NA
146 12 days 1030127.0 886695.8 RIDGE 5138
147 12 days 1017204.0 908242.7 NA
148 12 days 1032972.0 903934.3 19TH 4406
149 12 days 1029537.0 893621.6 NA
150 12 days 1025357.0 907701.4 NA
151 12 days 1033897.0 889429.4 ST LOUIS 5109
152 12 days 1022022.0 907595.8 13TH 1430
153 11 days 1017799.0 907619.9 9TH 205
154 11 days 1020329.0 899238.6 OLIVE 3037
155 11 days 1031357.0 905076.9 NA
156 11 days 997764.3 895610.9 NEBRASKA 4529
157 11 days 1026735.0 891120.7 NA
158 11 days 1007405.0 894425.6 NA
159 11 days 1035772.0 882305.7 WABADA 5969
160 11 days 1026184.0 890301.9 ENRIGHT 4550
161 11 days 1051786.0 894677.4 HOWELL 1181
162 11 days 1027321.0 888019.9 NA
163 11 days 1051778.0 894566.9 NA
164 11 days 1032520.0 880346.8 NA
165 11 days 1051838.0 895354.6 HOWELL 1115
166 10 days 1028775.0 900428.8 NA
167 10 days 1027075.0 896370.4 GARFIELD 4000
168 10 days 1022154.0 898673.6 NA
169 10 days 1004925.0 897760.9 CHEROKEE 2720
170 10 days 1026933.0 888149.1 AUBERT 773
171 10 days 1027279.0 891495.9 NA
172 10 days 1022136.0 905133.4 HOGAN 1800
173 10 days 998003.9 893209.4 VIRGINIA 4518
174 9 days 1010572.0 903703.8 ALLEN 1051
175 9 days 1025124.0 905021.8 BENTON 1933
176 9 days 1045249.0 887714.6 LALITE 6336
177 9 days 1037168.0 894947.6 NA
178 9 days 990701.9 888591.6 NA
179 9 days 1028552.0 881199.8 GOODFELLOW 853
180 9 days 993755.2 878508.2 GRAVOIS 7422
181 9 days 1046545.0 891148.8 NA
182 9 days 1068605.0 910845.4 EB 270 NA
183 9 days 1017616.0 876377.6 PARK 6763
184 9 days 1028716.0 890091.6 PAGE 4711
185 9 days 1013953.0 904086.3 NA
186 9 days 1031748.0 885691.9 DR MARTIN LUTHER KING 5378
187 9 days 1030986.0 885172.3 NA
188 9 days 1022057.0 901215.1 STODDARD 2800
189 8 days 1022134.0 909573.1 NA
190 8 days 1006156.0 896621.1 PENNSYLVANIA 3244
191 8 days 1030986.0 885172.3 NA
192 8 days 1040130.0 894837.9 NA
193 8 days 1040705.0 891528.7 ARLINGTON 4941
194 8 days 1036066.0 893029.3 KOSSUTH 4863
195 8 days 1024730.0 907940.6 NA
196 7 days 1018977.0 905530.4 NA
197 7 days 1030451.0 896704.6 WHITTIER 3047
198 7 days 1018061.0 895739.8 SCOTT 3560
199 7 days 1040680.0 890932.3 EMERSON 4921
200 7 days 1032299.0 886623.5 PATTON 5331
201 7 days 1034664.0 896014.3 LEE 4438
202 7 days 1033549.0 887857.3 UNION 2700
203 7 days 1026696.0 899404.5 MONTGOMERY 3461
204 7 days 1035119.0 883649.6 HIGHLAND 5807
205 6 days 1032339.0 900019.9 SHERMAN 3927
206 6 days 1027313.0 897869.5 COTTAGE 3834
207 6 days 1034257.0 901754.4 NA
208 6 days 1044313.0 891175.7 GILMORE 5276
209 6 days 1002248.0 887263.9 MORGANFORD 4522
210 6 days 1007882.0 895751.5 HARTFORD 3230
211 6 days 1033378.0 891132.4 EUCLID 2944
212 6 days 1031231.0 903749.4 PENROSE 2106
213 6 days 1032953.0 887081.5 WABADA 5330
214 6 days 1032241.0 900648.8 LEE 3833
215 6 days 1035883.0 883594.1 MAFFITT 5800
216 6 days 1028851.0 891679.1 DR MARTIN LUTHER KING 4582
217 5 days 992870.4 892982.7 PENNSYLVANIA 5913
218 5 days 1032898.0 894410.2 NA
219 5 days 1028603.0 888564.7 AUBERT 1200
220 5 days 1051408.0 896489.1 CANAAN 907
221 5 days 1030932.0 893627.2 NA
222 5 days 1032937.0 892505.8 ASHLAND 4710
223 5 days 1026577.0 894318.1 EVANS 4200
224 5 days 1037449.0 894152.3 NA
225 5 days 1002881.0 895726.6 NA
226 5 days 1029269.0 898082.8 LABADIE 3945
227 5 days 1026084.0 899342.4 NA
228 5 days 1028563.0 898133.8 VANDEVENTER 2816
229 5 days 1043141.0 890387.4 WREN 5015
230 5 days 1017271.0 903906.1 NA
231 5 days 1022183.0 899505.3 NA
232 5 days 989533.2 887239.5 SCHIRMER 800
233 5 days 1002406.0 891194.5 ALBERTA 3921
234 5 days 989042.8 889513.5 PENNSYLVANIA 7403
235 5 days 997804.3 891252.8 DELOR 3659
236 5 days 1033277.0 904660.4 BISSELL 1121
237 5 days 1011331.0 881563.1 DALTON 0
238 5 days 1049783.0 891947.9 MORA 8561
239 5 days 1028461.0 885269.8 NA
240 5 days 1031326.0 890200.3 HAMMETT 4851
241 5 days 1049854.0 895810.5 HALLS FERRY 8612
242 5 days 1033725.0 882188.8 NA
243 5 days 1046626.0 890521.4 SHULTE 6035
244 4 days 1033658.0 899795.6 NA
245 4 days 1027078.0 888592.4 EUCLID 785
246 4 days 1031732.0 902337.5 GRAND 4206
247 4 days 990479.7 888796.9 NA
248 4 days 1051754.0 896324.3 ELIAS 933
249 4 days 1047754.0 889373.8 NA
250 4 days 1048493.0 896371.1 CHURCH 8309
251 4 days 1033060.0 891237.8 LABADIE 4843
252 4 days 1043363.0 890705.5 WREN 5055
253 4 days 1020209.0 903370.6 21ST 715
254 4 days 996624.4 891625.1 LOUISIANA 5211
255 3 days 1024271.0 906633.1 NA
256 3 days 1041538.0 892153.7 ALCOTT 5200
257 3 days 1027247.0 903401.7 DODIER NA
258 3 days 1022198.0 907641.5 14TH 1430
259 3 days 1035428.0 893986.3 4949
260 3 days 1024089.0 908269.7 NA
261 3 days 1033423.0 897476.8 KOSSUTH 4235
262 3 days 1034813.0 903047.6 DE SOTO 1409
263 3 days 995171.7 893913.2 WALSH 308
264 3 days 1002376.0 893358.7 NA
265 3 days 1031432.0 891251.6 NA
266 3 days 990000.4 888462.2 NA
267 3 days 1003994.0 894935.3 VIRGINIA 3620
268 2 days 1015110.0 900117.9 RUTGER 2654
269 2 days 1051640.0 897259.3 ELIAS 835
270 2 days 1051479.0 895927.5 CANAAN 971
271 2 days 1040904.0 886178.6 NA
272 2 days 1035696.0 893689.0 KOSSUTH 4727
273 2 days 1016725.0 890017.6 NORFOLK 4250
274 2 days 1030720.0 896366.2 ASHLAND 4279
275 1 days 1010894.0 893626.0 SHENANDOAH 3658
276 1 days 1004511.0 898021.4 OHIO 3452
277 1 days 1004375.0 884540.4 CHIPPEWA 4939
278 1 days 1027553.0 893493.7 DR MARTIN LUTHER KING 4308
279 1 days 1001617.0 895896.4 PENNSYLVANIA 3942
280 1 days 1004029.0 888199.9 MORGANFORD 4254
281 1 days 1037577.0 885193.6 SELBER 5830
282 1 days 1043688.0 891512.5 WREN 5270
283 1 days 1003043.0 890577.3 DUNNICA 3946
284 1 days 1023898.0 906643.6 NA
285 1 days 1019893.0 910547.7 2ND 999
286 1 days 1049007.0 890159.1 GOODFELLOW 5961
287 1 days 1034124.0 895794.9 FARLIN 4447
288 1 days 1052972.0 896665.4 RIVERVIEW 911
289 0 days 1040599.0 893346.5 GERALDINE 5304
290 0 days 1035764.0 882107.0 WABADA 5962
291 0 days 1033922.0 888254.7 TERRY 5252
292 0 days 1036786.0 886544.7 CLARA 3340
293 0 days 1042473.0 892352.3 DAVISON 5271
294 0 days 995260.5 893647.8 EICHELBERGER 411
295 0 days 995670.8 891021.7 NA
296 0 days 1031520.0 885578.9 ARLINGTON 1460
297 0 days 1038456.0 900558.8 NA
298 -1 days 1031068.0 885126.1 ARLINGTON 1401
299 -1 days 1028747.0 898586.3 LABADIE 3800
300 -1 days 1010081.0 903858.6 NA
301 -1 days 1016086.0 908223.8 CLARK 601
302 -1 days 1032564.0 881482.7 NA
303 -1 days 1017361.0 890066.3 MANCHESTER 4238
304 -1 days 1035157.0 902483.6 CONDE 5220
305 -1 days 1036030.0 882441.9 HIGHLAND 5900
306 -1 days 1043154.0 890135.3 NA
307 -1 days 1036149.0 891990.5 FARLIN 4950
308 -1 days 1022299.0 906762.9 CASS 1415
309 -1 days 989055.9 889596.9 UPTON 225
310 -1 days 1000754.0 895093.4 MICHIGAN 4100
311 -2 days 997429.1 891964.9 TENNESSEE 4758
312 -2 days 1037384.0 885274.5 GOODFELLOW 5713
313 -2 days 1003002.0 895591.7 NA
314 -2 days 1029828.0 885989.0 MINERVA 5232
315 -2 days 1035240.0 898489.6 ATHLONE 4430
316 -2 days 1051865.0 896562.4 HARLAN 917
317 -2 days 1041712.0 893530.7 CLAXTON 5357
318 -2 days 1039648.0 885117.3 FERRIS 5911
319 -2 days 1028291.0 891706.2 NA
320 -2 days 1028254.0 890506.0 PAGE 4634
321 -2 days 1035149.0 882181.7 NA
322 -2 days 1013424.0 904108.4 NA
323 -3 days 1003486.0 896527.1 NEBRASKA 3646
324 -3 days 1032899.0 894125.9 LEXINGTON 4534
325 -3 days 1033526.0 899422.0 CLAY 4221
326 -3 days 1034569.0 886330.5 NA
327 NA days 1008027.0 901258.3 CONGRESS 1909
328 NA days 1032246.0 900444.8 LEE 3856
329 NA days 1028281.0 894047.3 COTE BRILLIANTE 4200
330 NA days 1010480.0 895630.6 LOUISIANA 2328
331 NA days 992407.4 890121.1 IDAHO 6602
332 NA days 1034773.0 881698.6 NA
333 NA days 1031673.0 884752.1 BELT 1439
334 NA days 1032225.0 882995.8 BLACKSTONE 1387
335 NA days 1028866.0 877725.8 NA
336 NA days 1002881.0 895726.6 NA
337 NA days 1017651.0 909633.8 NA
338 NA days 1042247.0 890065.3 PLOVER 4938
339 NA days 1046874.0 889008.8 GOODFELLOW 5517
340 NA days 1030180.0 888420.4 KINGSHIGHWAY 1408
341 NA days 1008747.0 887198.5 LACKLAND 3139
342 NA days 1048551.0 893434.8 NA
343 NA days 1047618.0 897453.9 BITTNER 732
344 NA days 1028958.0 887970.3 MAPLE 5009
LocationComment LocationName
1 REAR
2
3
4 AUTO ZONE
5
6
7 DOLLAR GENERAL
8
9
10
11
12
13
14
15 ON STREET ON STREET
16
17 NBC LOUNGE
18
19
20
21 WEST PARKING LOT M&A LIQUOR STORE
22 MOUNT PLEASANT PARK
23
24 PAPA JOHNS PIZZA
25 APARTMENT BUILDING
26 STREET
27 THE OTHER PLACE LOUNGE
28
29
30 HOTEL- FIRST WESTERN INN
31
32 RESIDENCE RESIDENCE
33
34
35
36
37
38
39
40 ALLEY ADJACENT TO SIDE WINDOW
41 LONDON S BOARDING HOUSE
42
43 REAR ALLEY
44
45
46
47
48
49
50 CLINTON-PEABODY HOUSING COMPLEX
51 M & A MARKET PARKING LOT
52 JUST INSIDE OF TREE LINE IN REAR OF
53
54 @AMEREN - 158
55
56
57
58
59
60 SECOND FLOOR
61
62
63
64
65
66
67
68 REAR YARD OF VACANT RESIDENCE
69
70
71
72
73
74
75 GRAVOIS PARK
76
77
78
79 REAR
80
81 MIDWEST PETROLEUM
82
83
84 SOUTH SIDE OF AMBERG PARK
85
86 APT A
87
88
89
90
91
92
93 CAR WASH AND AUTOMOBILE DETAILING BU BIG P. S CAR WASH AND DETAILING
94
95
96
97 APT B
98
99
100 ST. LOUIS FISH & CHICKEN
101 IN STREET MURPHY PARK APARTMENTS
102 UPPER LEVEL CLUB
103
104
105
106
107
108
109 STREET
110
111 14TH FLOOR
112
113
114
115
116
117
118
119
120
121 @BP GAS STATION PARKING LOT
122
123
124
125
126
127
128
129
130
131
132 VACANT LOT
133
134
135
136 IN STREET
137
138 BEST AUTO PLEX
139 GROCERY STORE PRICE CHOPPER
140 5401 MISSOURI ROUTE 30 LUCKY DUCK RESTAURANT
141 @SCHNUCKS - CITY PLAZA ON UNION
142 TWO-FAMILY APARTMENT BLDG
143 CONOCO GAS STATION
144 EAST SIDEWALK
145
146
147
148
149
150
151 NORTH ALLEY
152 CASS BANK
153
154 INTERIOR/SIDEWALK/PARKING LOT/STREET OLIVE BAR
155 WINDSOR PARK
156 STREET
157
158
159 VACANT RESIDENCE
160
161
162 CROWN FOOD MART
163
164 JULIAN AT HODIAMONT
165
166 BING LAU
167
168
169
170
171
172 REAR
173
174
175 APT A
176 REAR
177
178
179
180 APARTMENT COMPLEX GRAVOIS PLACE APARTMENTS
181 REAR ALLEY
182
183
184
185 ST. LOUIS HOUSING AUTHORITY CLINTON PEABODY PUBLIC HOUSING COMP
186
187
188 REAR
189
190
191
192
193
194
195
196
197
198
199
200
201 REAR GARAGE
202
203
204
205
206
207
208
209 THE ELLENWOOD BUILDING
210 @ROOSEVELT HIGH SCHOOL
211
212 ALLEY
213
214
215
216
217
218
219
220
221 EAST SIDE OF STREET IN FRONT OF A VA
222
223
224
225
226 2ND FLOOR
227
228 MIDWEST PETROLEUM
229
230 STREET
231
232
233
234
235
236
237 DUPLEX ATTACHED TO 2641 DALTON
238
239 HAROLD S CHOP SUEY
240
241 APARTMENT RIVERVIEW APARTMENTS
242
243
244
245
246 @HOTEL- ECONOMY INN
247
248
249
250 ALLEY
251
252
253 APT 412
254
255 NUMERICAL ADDRESS OF THE PRIMARY SC
256 REAR ALLEY
257
258 @DOMINOS PIZZA - N 13TH
259
260
261 ALLEY/DUMPSTER
262 INTERSECTION OF E DE SOTO AVENUE AND
263 APT C
264
265
266 VACANT LOT
267
268 APARTMENT COMPLEX CAROLINE PLACE APARTMENTS
269
270
271
272 REAR ALLEY
273
274
275
276
277 APT 3E
278 PARKING LOT MARTIN LUTHER KING PLAZA
279
280
281
282
283
284
285 STREET
286
287
288
289
290
291
292
293
294 REAR SCHMIDT EQUIPMENT AND SUPPLY
295
296
297 LOVES TRUCK STOP
298 HANK S PACKAGE LIQUOR
299
300 @BAR-BASTILLE BASTILLE
301 BALL PARK VILLAGE BALLPARK VILLAGE / BUDWEISER BREW H
302
303
304
305
306
307
308 ON STREET
309 1ST FL
310
311
312 GOODFELLOW PLACE APARTMENTS
313
314
315
316
317
318
319
320
321
322 PARKING LOT CLINTON PEABODY
323 REAR EAST ALLEY
324
325
326 REAR
327
328
329 VACANT LOT
330 1ST FLOOR
331
332
333 STREET
334
335 VACANT LOT
336
337 MANSION HOUSE
338
339 REAR ALLEY
340
341
342
343
344 REAR DETACHED GARAGE- VACANT RESIDEN
Neighborhood ILEADSStreet ILEADSAddress Description
1 3 S 38TH ST 5215 HOMICIDE
2 17 ITASKA ST 3111 HOMICIDE
3 1 HOLLY HILLS AVE 629 HOMICIDE
4 53 N KINGSHIGHWAY BLVD 1225 HOMICIDE
5 68 W GREEN LEA PL 4132 HOMICIDE
6 63 N 14TH ST 1908 HOMICIDE
7 67 N GRAND BLVD 4038 HOMICIDE
8 74 GIMBLIN ST 1018 HOMICIDE
9 57 ALDINE AVE 4337 HOMICIDE
10 50 BURD AVE 2524 HOMICIDE
11 59 FRANKLIN AVE 3114 HOMICIDE
12 65 KNAPP ST 3245 HOMICIDE
13 57 W COTE BRILLIANTE AVE 4229 HOMICIDE
14 8 KINSEY PL 6272 HOMICIDE
15 50 KENNERLY AVE 5972 HOMICIDE
16 50 GROVER ST 2521 HOMICIDE
17 56 DR MARTIN LUTHER KING DR 4621 HOMICIDE
18 50 COTE BRILLIANTE AVE 5895 HOMICIDE
19 48 HAMILTON AVE 1021 HOMICIDE
20 36 N 15TH ST 709 HOMICIDE
21 68 NATURAL BRIDGE AVE 4231 HOMICIDE
22 17 MICHIGAN AVE 4461 HOMICIDE
23 74 BADEN AVE 724 HOMICIDE
24 19 S GRAND BLVD 3630 HOMICIDE
25 14 HEREFORD ST 3322 HOMICIDE
26 19 S COMPTON AVE 3751 HOMICIDE
27 51 DR MARTIN LUTHER KING DR 5084 HOMICIDE
28 1 VIRGINIA AVE 5301 HOMICIDE
29 78 CLARA AVE 1401 HOMICIDE
30 64 N BROADWAY 4828 HOMICIDE
31 57 BILLUPS AVE 1707 HOMICIDE
32 74 LOWELL ST 8861 HOMICIDE
33 78 MINERVA AVE 5634 HOMICIDE
34 17 OSCEOLA ST 3110 HOMICIDE
35 60 RAUSCHENBACH AVE 3117 HOMICIDE
36 70 MCARTHUR AVE 5910 HOMICIDE
37 59 DR MARTIN LUTHER KING DR 3731 HOMICIDE
38 74 RIVERVIEW BLVD 1124 HOMICIDE
39 15 POTOMAC ST 3949 HOMICIDE
40 5 WALLACE AVE 4314 HOMICIDE
41 50 BURD AVE 2728 HOMICIDE
42 50 UNION BLVD 2611 HOMICIDE
43 49 VERNON AVE 5326 HOMICIDE
44 56 N NEWSTEAD AVE 3224 HOMICIDE
45 59 GAMBLE ST 2900 HOMICIDE
46 76 ERA AVE 5530 HOMICIDE
47 15 CONNECTICUT ST 3634 HOMICIDE
48 50 SAINT LOUIS AVE 5920 HOMICIDE
49 71 EMERSON AVE 5416 HOMICIDE
50 33 DILLON DR 1124 HOMICIDE
51 68 NATURAL BRIDGE AVE 4231 HOMICIDE
52 69 CARRIE AVE 4539 HOMICIDE
53 60 N 13TH ST 1513 HOMICIDE
54 71 SHREVE AVE 5318 HOMICIDE
55 19 S COMPTON AVE 3720 HOMICIDE
56 50 PALM ST 5556 HOMICIDE
57 61 COCHRAN PL 1459 HOMICIDE
58 56 GARFIELD AVE 3915 HOMICIDE
59 72 THRUSH AVE 5400 HOMICIDE
60 16 MERAMEC ST 3711 HOMICIDE
61 76 FLOY AVE 5592 HOMICIDE
62 72 ROBIN AVE 5598 HOMICIDE
63 2 W DAVIS ST 547 HOMICIDE
64 74 SWITZER AVE 951 HOMICIDE
65 56 DICK GREGORY PL 1524 HOMICIDE
66 18 CHIPPEWA ST 2605 HOMICIDE
67 65 N 21ST ST 3915 HOMICIDE
68 66 E PRAIRIE AVE 2009 HOMICIDE
69 74 FREDERICK ST 8216 HOMICIDE
70 77 JOSEPHINE BAKER AVE 700 HOMICIDE
71 50 HIGHLAND AVE 5627 HOMICIDE
72 74 RIVERVIEW BLVD 1052 HOMICIDE
73 72 W FLORISSANT AVE 5728 HOMICIDE
74 74 N BROADWAY 8216 HOMICIDE
75 19 LOUISIANA AVE 3513 HOMICIDE
76 69 FARLIN AVE 4892 HOMICIDE
77 72 PLOVER AVE 4936 HOMICIDE
78 69 N NEWSTEAD AVE 4149 HOMICIDE
79 51 ENRIGHT AVE 5048 HOMICIDE
80 56 LABADIE AVE 4435 HOMICIDE
81 59 N VANDEVENTER 2815 HOMICIDE
82 17 MINNESOTA AVE 4629 HOMICIDE
83 55 HIGHLAND AVE 4900 HOMICIDE
84 16 KEOKUK ST 3836 HOMICIDE
85 56 LEXINGTON AVE 3963 HOMICIDE
86 69 KOSSUTH AVE 4834 HOMICIDE
87 68 RED BUD AVE 3980 HOMICIDE
88 15 GILES AVE 3521 HOMICIDE
89 16 LOUISIANA AVE 4114 HOMICIDE
90 67 BAILEY AVE 3215 HOMICIDE
91 58 PENDLETON AVE 924 HOMICIDE
92 33 HICKORY LN 1435 HOMICIDE
93 77 N VANDEVENTER AVE 1420 HOMICIDE
94 72 ROBIN AVE 5428 HOMICIDE
95 79 E CARRIE AVE / I 70 WESTBOUND 0 HOMICIDE
96 54 MCMILLAN AVE 4561 HOMICIDE
97 18 WISCONSIN AVE 3853 HOMICIDE
98 59 PRAIRIE AVE 3614 HOMICIDE
99 17 MICHIGAN AVE 4622 HOMICIDE
100 76 GOODFELLOW BLVD 5000 HOMICIDE
101 61 MURPHY PARK DR 1847 HOMICIDE
102 59 N GRAND BLVD 2546 HOMICIDE
103 78 MINERVA AVE 5363 HOMICIDE
104 17 MICHIGAN AVE 4404 HOMICIDE
105 16 CALIFORNIA AVE 4056 HOMICIDE
106 16 MERAMEC ST 3145 HOMICIDE
107 1 IDAHO AVE 5417 HOMICIDE
108 2 VERMONT AVE 7315 HOMICIDE
109 57 ALDINE AVE 4349 HOMICIDE
110 56 PALM ST 3926 HOMICIDE
111 38 PARKVIEW PL 4921 HOMICIDE
112 53 N KINGSHIGHWAY BLVD 900 HOMICIDE
113 56 N MARKET ST 4647 HOMICIDE
114 59 N MARKET ST 3800 HOMICIDE
115 30 VIRGINIA AVE 3429 HOMICIDE
116 54 MCMILLAN AVE 4735 HOMICIDE
117 56 N TAYLOR AVE 3120 HOMICIDE
118 65 N FLORISSANT AVE 3330 HOMICIDE
119 76 GOODFELLOW BLVD 5003 HOMICIDE
120 16 KLOCKE ST 3400 HOMICIDE
121 76 GOODFELLOW BLVD 5003 HOMICIDE
122 55 N EUCLID AVE 2944 HOMICIDE
123 66 E JOHN AVE 1439 HOMICIDE
124 73 NORTH POINTE BLVD 6139 HOMICIDE
125 15 GUSTINE AVE 3619 HOMICIDE
126 5 DELOR ST 4254 HOMICIDE
127 58 C D BANKS AVE 4158 HOMICIDE
128 65 N 14TH ST 3504 HOMICIDE
129 69 PENROSE ST 4481 HOMICIDE
130 50 THEODOSIA AVE 5606 HOMICIDE
131 69 SHREVE AVE 4049 HOMICIDE
132 56 GARFIELD AVE 4040 HOMICIDE
133 37 SAMUEL SHEPARD DR 2946 HOMICIDE
134 84 I 70 WESTBOUND / N KINGSHIGHWA 0 HOMICIDE
135 50 ROOSEVELT PL 5816 HOMICIDE
136 16 CHIPPEWA ST 3013 HOMICIDE
137 68 CLARENCE AVE 4415 HOMICIDE
138 55 DR MARTIN LUTHER KING DR 4815 HOMICIDE
139 50 GOODFELLOW BLVD 2747 HOMICIDE
140 5 GRAVOIS AVE 5401 HOMICIDE
141 50 UNION BLVD 3431 HOMICIDE
142 69 RICHARD PL 4625 HOMICIDE
143 17 S BROADWAY 4355 HOMICIDE
144 55 MARCUS AVE 2613 HOMICIDE
145 76 SHERRY AVE 6120 HOMICIDE
146 51 RIDGE AVE 5140 HOMICIDE
147 35 CHESTNUT ST 714 HOMICIDE
148 66 N 19TH ST 4406 HOMICIDE
149 57 SAINT FERDINAND AVE 4370 HOMICIDE
150 63 N MARKET PL 1101 HOMICIDE
151 52 ST LOUIS AVE 5105 HOMICIDE
152 62 N 13TH ST 1420 HOMICIDE
153 35 N 9TH ST 205 HOMICIDE
154 37 OLIVE ST 3037 HOMICIDE
155 65 BLAIR AVE 4109 HOMICIDE
156 17 NEBRASKA AVE 4528 HOMICIDE
157 54 MCMILLAN AVE 4503 HOMICIDE
158 25 ARKANSAS AVE 3170 HOMICIDE
159 50 WABADA AVE 5969 HOMICIDE
160 54 ENRIGHT AVE 4550 HOMICIDE
161 74 HOWELL ST 1181 HOMICIDE
162 53 N KINGSHIGHWAY BLVD 930 HOMICIDE
163 74 HALLS FERRY RD 9006 HOMICIDE
164 48 JULIAN AVE 5985 HOMICIDE
165 74 HOWELL ST 1115 HOMICIDE
166 59 N GRAND BLVD 3101 HOMICIDE
167 56 GARFIELD AVE 4012 HOMICIDE
168 77 FRANKLIN AVE 3311 HOMICIDE
169 19 IOWA AVE 3420 HOMICIDE
170 53 AUBERT AVE 773 HOMICIDE
171 54 NEWBERRY TER 4502 HOMICIDE
172 61 HOGAN ST 1320 HOMICIDE
173 16 ALASKA AVE 4630 HOMICIDE
174 21 ALLEN AVE 1051 HOMICIDE
175 60 BENTON ST 1933 HOMICIDE
176 76 SHERRY AVE 6341 HOMICIDE
177 69 SEXAUER AVE 4420 HOMICIDE
178 1 IDAHO AVE 7138 HOMICIDE
179 48 GOODFELLOW BLVD 853 HOMICIDE
180 4 GRAVOIS AVE 7422 HOMICIDE
181 76 SUMMIT AVE 5629 HOMICIDE
182 75 RIVERVIEW DR / WB 270 0 HOMICIDE
183 44 W PARK AVE 6763 HOMICIDE
184 54 PAGE BLVD 4711 HOMICIDE
185 33 HICKORY LN 1468 HOMICIDE
186 78 DR MARTIN LUTHER KING DR 5390 HOMICIDE
187 78 RIDGE AVE 5368 HOMICIDE
188 59 STODDARD ST 2800 HOMICIDE
189 63 N 7TH ST / I 70 WESTBOUND 0 HOMICIDE
190 30 PENNSYLVANIA AVE 3244 HOMICIDE
191 78 RIDGE AVE 5401 HOMICIDE
192 71 N KINGSHIGHWAY BLVD 5406 HOMICIDE
193 71 ARLINGTON AVE 4943 HOMICIDE
194 69 KOSSUTH AVE 4863 HOMICIDE
195 63 CLINTON ST 1200 HOMICIDE
196 36 LOCUST ST 1527 HOMICIDE
197 56 WHITTIER ST 3047 HOMICIDE
198 37 S GRAND BLVD 715 HOMICIDE
199 72 EMERSON AVE 4921 HOMICIDE
200 50 PATTON AVE 5331 HOMICIDE
201 69 LEE AVE 4440 HOMICIDE
202 52 UNION BLVD 2700 HOMICIDE
203 59 MONTGOMERY ST 3561 HOMICIDE
204 50 HIGHLAND AVE 5824 HOMICIDE
205 67 SHERMAN PL 3921 HOMICIDE
206 59 COTTAGE AVE 3842 HOMICIDE
207 66 E LINTON AVE 2157 HOMICIDE
208 72 GILMORE AVE 5276 HOMICIDE
209 5 MORGANFORD RD 4528 HOMICIDE
210 25 HARTFORD ST 3230 HOMICIDE
211 55 N EUCLID AVE 2944 HOMICIDE
212 65 PENROSE ST 2115 HOMICIDE
213 50 WABADA AVE 5324 HOMICIDE
214 67 LEE AVE 3838 HOMICIDE
215 50 MAFFITT AVE 5870 HOMICIDE
216 56 DR MARTIN LUTHER KING DR 4557 HOMICIDE
217 1 PENNSYLVANIA AVE 5913 HOMICIDE
218 56 LEXINGTON AVE 4476 HOMICIDE
219 53 AUBERT AVE 1224 HOMICIDE
220 74 CANAAN AVE 915 HOMICIDE
221 57 N TAYLOR AVE 2814 HOMICIDE
222 55 MARCUS AVE 3061 HOMICIDE
223 58 EVANS AVE 4220 HOMICIDE
224 69 CARTER AVE 4836 HOMICIDE
225 16 MINNESOTA AVE 3754 HOMICIDE
226 56 LABADIE AVE 3945 HOMICIDE
227 59 BACON ST 2409 HOMICIDE
228 56 N VANDEVENTER AVE 2821 HOMICIDE
229 72 WREN AVE 5019 HOMICIDE
230 36 S 18TH ST / CLARK AVE 0 HOMICIDE
231 59 BELL AVE 3100 HOMICIDE
232 1 SCHIRMER ST 802 HOMICIDE
233 16 ALBERTA ST 3921 HOMICIDE
234 2 PENNSYLVANIA AVE 7403 HOMICIDE
235 16 DELOR ST 3659 HOMICIDE
236 66 RANDALL PL 4426 HOMICIDE
237 13 DALTON AVE 2639 HOMICIDE
238 73 MORA LN 8561 HOMICIDE
239 51 UNION BLVD 1122 HOMICIDE
240 55 HAMMETT PL 4851 HOMICIDE
241 74 HALLS FERRY RD 8612 HOMICIDE
242 78 HAMILTON AVE 1452 HOMICIDE
243 76 SHULTE AVE 6035 HOMICIDE
244 68 CARTER AVE 4042 HOMICIDE
245 53 N EUCLID AVE 785 HOMICIDE
246 67 N GRAND BLVD 4206 HOMICIDE
247 1 ALABAMA AVE 7146 HOMICIDE
248 74 ELIAS AVE 933 HOMICIDE
249 76 ACME AVE 5636 HOMICIDE
250 74 CHURCH RD 8309 HOMICIDE
251 55 LABADIE AVE 4843 HOMICIDE
252 72 WREN AVE 5055 HOMICIDE
253 36 N 21ST ST 715 HOMICIDE
254 1 LOUISIANA AVE 5205 HOMICIDE
255 63 CLINTON ST 1455 HOMICIDE
256 72 EMERSON AVE 5201 HOMICIDE
257 60 E DODIER ST 2511 HOMICIDE
258 62 N 13TH ST 1430 HOMICIDE
259 69 MARCUS AVE 4100 HOMICIDE
260 63 CHAMBERS ST 1120 HOMICIDE
261 68 E KOSSUTH AVE 4235 HOMICIDE
262 66 E DE SOTO AVE 1401 HOMICIDE
263 17 WALSH ST 308 HOMICIDE
264 16 S GRAND BLVD 3900 HOMICIDE
265 55 CUPPLES PL 4741 HOMICIDE
266 2 VERMONT AVE 7343 HOMICIDE
267 19 VIRGINIA AVE 3620 HOMICIDE
268 31 HICKORY ST 2651 HOMICIDE
269 74 ELIAS AVE 827 HOMICIDE
270 74 CANAAN AVE 971 HOMICIDE
271 70 GOODFELLOW BLVD 4301 HOMICIDE
272 69 SHREVE AVE 4106 HOMICIDE
273 39 NORFOLK AVE 4247 HOMICIDE
274 56 NEW ASHLAND PL 3100 HOMICIDE
275 27 SHENANDOAH AVE 3658 HOMICIDE
276 19 OHIO AVE 3454 HOMICIDE
277 14 CHIPPEWA ST 4939 HOMICIDE
278 58 DR MARTIN LUTHER KING DR 4308 HOMICIDE
279 16 PENNSYLVANIA AVE 3942 HOMICIDE
280 15 MERAMEC ST 4255 HOMICIDE
281 50 GOODFELLOW BLVD 3351 HOMICIDE
282 72 WREN AVE 5270 HOMICIDE
283 16 DUNNICA AVE 3946 HOMICIDE
284 60 MADISON ST 1501 HOMICIDE
285 35 CARR ST 202 HOMICIDE
286 73 GOODFELLOW BLVD 5961 HOMICIDE
287 69 FARLIN AVE 4435 HOMICIDE
288 74 RIVERVIEW BLVD 907 HOMICIDE
289 71 GERALDINE AVE 5300 HOMICIDE
290 50 WABADA AVE 5962 HOMICIDE
291 52 TERRY AVE 5252 HOMICIDE
292 50 CLARA AVE 3340 HOMICIDE
293 72 DAVISON AVE 5271 HOMICIDE
294 17 EICHELBERGER ST 411 HOMICIDE
295 1 BATES ST 1005 HOMICIDE
296 78 ARLINGTON AVE 1460 HOMICIDE
297 79 N BROADWAY 6124 HOMICIDE
298 78 ARLINGTON AVE 1401 HOMICIDE
299 59 LABADIE AVE 3850 HOMICIDE
300 21 RUSSELL AVE 1027 HOMICIDE
301 35 CLARK AVE 601 HOMICIDE
302 78 HAMILTON TER 5946 HOMICIDE
303 39 MANCHESTER AVE 4229 HOMICIDE
304 66 CONDE ST 5220 HOMICIDE
305 50 HIGHLAND AVE 5971 HOMICIDE
306 72 ROBIN AVE 5016 HOMICIDE
307 69 FARLIN AVE 4950 HOMICIDE
308 61 COCHRAN PL 1457 HOMICIDE
309 2 UPTON ST 225 HOMICIDE
310 16 MICHIGAN AVE 4108 HOMICIDE
311 16 TENNESSEE AVE 4758 HOMICIDE
312 50 GOODFELLOW PL 5713 HOMICIDE
313 19 MINNESOTA AVE 3741 HOMICIDE
314 51 MINERVA AVE 5228 HOMICIDE
315 68 ATHLONE AVE 4448 HOMICIDE
316 74 HARLAN AVE 916 HOMICIDE
317 71 CLAXTON AVE 5381 HOMICIDE
318 70 FERRIS AVE 5911 HOMICIDE
319 54 EVANS AVE 4527 HOMICIDE
320 54 PAGE BLVD 4630 HOMICIDE
321 50 COTE BRILLIANTE AVE 5951 HOMICIDE
322 33 RUTGER LN 1417 HOMICIDE
323 19 NEBRASKA AVE 3646 HOMICIDE
324 56 LEXINGTON AVE 4520 HOMICIDE
325 68 CLAY AVE 4221 HOMICIDE
326 50 BELT AVE 2738 HOMICIDE
327 22 CONGRESS ST 1909 HOMICIDE
328 67 LEE AVE 3836 HOMICIDE
329 57 W COTE BRILLIANTE AVE 4267 HOMICIDE
330 25 LOUISIANA AVE 2328 HOMICIDE
331 1 IDAHO AVE 6440 HOMICIDE
332 50 DR MARTIN LUTHER KING DR 5971 HOMICIDE
333 78 BELT AVE 1431 HOMICIDE
334 78 BLACKSTONE AVE 1387 HOMICIDE
335 48 N SKINKER BLVD 882 HOMICIDE
336 16 MINNESOTA AVE 3754 HOMICIDE
337 35 N 4TH ST 300 HOMICIDE
338 72 PLOVER AVE 4938 HOMICIDE
339 76 EMMA AVE 6307 HOMICIDE
340 53 N KINGSHIGHWAY BLVD 1408 HOMICIDE
341 15 LACKLAND AVE 3144 HOMICIDE
342 74 ETON LN 1545 HOMICIDE
343 74 BITTNER ST 732 HOMICIDE
344 51 MAPLE AVE 5009 HOMICIDE
District Crime DateOccur CodedMonth Complaint
1 1 10000 2017-02-22 23:45:00 2018-02-28 18-008475
2 1 10000 2018-03-17 23:00:00 2018-04-28 18-012176
3 1 10000 2019-06-27 01:48:00 2019-07-28 19-030335
4 5 10000 2019-10-30 21:07:00 2019-11-28 19-054615
5 6 10000 2018-12-01 17:37:00 2018-12-28 18-056958
6 4 10000 2018-11-01 23:00:00 2018-11-28 18-052272
7 4 10000 2018-11-01 13:07:00 2018-11-28 18-052152
8 6 10000 2018-08-01 23:03:00 2018-08-28 18-035664
9 5 10000 2018-05-01 21:20:00 2018-05-28 18-019411
10 5 10000 2018-05-01 12:47:00 2018-05-28 18-019311
11 4 10000 2018-05-01 11:00:00 2018-05-28 18-020625
12 4 10000 2018-03-01 18:30:00 2018-03-28 18-009700
13 5 10000 2018-03-01 08:50:00 2018-03-28 18-009431
14 2 10000 2018-02-01 16:30:00 2018-02-28 18-005350
15 5 10000 2018-01-01 17:00:00 2018-01-28 18-000142
16 5 10000 2019-04-01 15:29:00 2019-04-28 19-014217
17 5 10000 2019-06-01 01:03:00 2019-06-28 19-025297
18 5 10000 2019-06-01 02:48:00 2019-06-28 19-025312
19 5 10000 2019-09-01 04:15:00 2019-09-28 19-043120
20 4 10000 2019-11-01 02:22:00 2019-11-28 19-054833
21 6 10000 2018-12-02 15:16:00 2018-12-28 18-057082
22 1 10000 2018-08-02 14:02:00 2018-08-28 18-035796
23 6 10000 2018-07-02 22:00:00 2018-07-28 18-030399
24 3 10000 2018-06-02 01:34:00 2018-06-28 18-024776
25 2 10000 2018-05-02 19:00:00 2018-05-28 18-019792
26 3 10000 2018-05-02 15:01:00 2018-05-28 18-019544
27 5 10000 2018-02-02 23:25:00 2018-02-28 18-005382
28 1 10000 2019-06-02 08:52:00 2019-06-28 19-025531
29 5 10000 2019-06-02 10:05:00 2019-06-28 19-025525
30 6 10000 2018-12-03 01:24:00 2018-12-28 18-057161
31 5 10000 2018-07-03 17:34:00 2018-07-28 18-030526
32 6 10000 2018-04-03 18:00:00 2018-04-28 18-014712
33 5 10000 2019-01-03 13:36:00 2019-01-28 19-000429
34 1 10000 2019-02-03 19:30:00 2019-02-28 19-005180
35 4 10000 2019-06-03 06:03:00 2019-06-28 19-025632
36 6 10000 2019-07-03 08:31:00 2019-07-28 19-031517
37 4 10000 2019-10-03 15:02:00 2019-10-28 19-049602
38 6 10000 2018-11-04 21:05:00 2018-11-28 18-052745
39 2 10000 2018-07-04 23:45:00 2018-07-28 18-030719
40 1 10000 2018-06-04 21:43:00 2018-06-28 18-025251
41 5 10000 2019-03-04 20:03:00 2019-03-28 19-009857
42 5 10000 2019-05-04 03:13:00 2019-05-28 19-020139
43 5 10000 2019-05-04 10:40:00 2019-05-28 19-020183
44 6 10000 2019-06-04 01:40:00 2019-06-28 19-025830
45 4 10000 2019-07-04 21:55:00 2019-07-28 19-031835
46 6 10000 2019-10-04 23:45:00 2019-10-28 19-049868
47 2 10000 2018-12-05 16:45:00 2018-12-28 18-057634
48 5 10000 2018-09-05 08:18:00 2018-09-28 18-041936
49 6 10000 2018-08-05 21:10:00 2018-08-28 18-036365
50 3 10000 2018-07-05 21:26:00 2018-07-28 18-030894
51 6 10000 2018-07-05 19:44:00 2018-07-28 18-030877
52 6 10000 2018-07-05 11:18:00 2018-07-28 18-030812
53 4 10000 2018-07-05 00:20:00 2018-07-28 18-030729
54 6 10000 2018-03-05 00:45:00 2018-03-28 18-009971
55 3 10000 2018-01-05 21:45:00 2018-01-28 18-000847
56 5 10000 2019-03-05 13:00:00 2019-03-28 19-009999
57 4 10000 2019-08-05 01:18:00 2019-08-28 19-037801
58 5 10000 2019-09-05 00:51:00 2019-09-28 19-043868
59 6 10000 2019-09-05 13:25:00 2019-09-28 19-043969
60 1 10000 2019-11-05 09:26:00 2019-11-28 19-055608
61 6 10000 2018-09-06 19:09:00 2018-09-28 18-042231
62 6 10000 2018-07-06 21:42:00 2018-07-28 18-031078
63 1 10000 2018-06-06 05:40:00 2018-06-28 18-025503
64 6 10000 2018-05-06 16:25:00 2018-05-28 18-020241
65 5 10000 2018-04-06 20:10:00 2018-04-28 18-015241
66 3 10000 2019-01-06 15:30:00 2019-01-28 19-000915
67 4 10000 2019-04-06 15:35:00 2019-04-28 19-015072
68 6 10000 2019-04-06 21:50:00 2019-04-28 19-015262
69 6 10000 2019-05-06 23:15:00 2019-05-28 19-020657
70 4 10000 2019-06-06 11:58:00 2019-06-28 19-026300
71 5 10000 2019-11-06 23:00:00 2019-11-28 19-055927
72 6 10000 2018-08-07 22:30:00 2018-08-28 18-036817
73 6 10000 2018-06-07 04:04:00 2018-06-28 18-025671
74 6 10000 2019-01-07 22:09:00 2019-01-28 19-001140
75 3 10000 2019-07-07 01:45:00 2019-07-28 19-032275
76 6 10000 2019-07-07 13:56:00 2019-07-28 19-032327
77 6 10000 2019-08-07 16:26:00 2019-08-28 19-038332
78 6 10000 2019-09-07 00:45:00 2019-09-28 19-044294
79 5 10000 2019-10-07 10:01:00 2019-10-28 19-050231
80 6 10000 2018-09-08 20:39:00 2018-09-28 18-042567
81 4 10000 2018-08-08 22:35:00 2018-08-28 18-037014
82 1 10000 2018-07-08 19:20:00 2018-07-28 18-031390
83 5 10000 2019-02-08 11:55:00 2019-02-28 19-005986
84 1 10000 2019-04-08 18:30:00 2019-04-28 19-015480
85 6 10000 2019-05-08 21:28:00 2019-05-28 19-021058
86 6 10000 2019-05-08 21:35:00 2019-05-28 19-021064
87 6 10000 2019-06-08 22:30:00 2019-06-28 19-026743
88 2 10000 2019-10-08 21:23:00 2019-10-28 19-050574
89 3 10000 2018-12-09 00:01:00 2018-12-28 18-058563
90 4 10000 2018-07-09 21:12:00 2018-07-28 18-031594
91 5 10000 2018-03-09 15:00:00 2018-03-28 18-010716
92 3 10000 2018-02-09 21:09:00 2018-02-28 18-006392
93 4 10000 2018-01-09 17:31:00 2018-01-28 18-001431
94 6 10000 2019-01-09 00:10:00 2019-01-28 19-001348
95 6 10000 2019-01-09 22:00:00 2019-01-28 19-001524
96 5 10000 2019-04-09 17:58:00 2019-04-28 19-015697
97 3 10000 2019-04-09 22:45:00 2019-04-28 19-015726
98 4 10000 2019-06-09 15:56:00 2019-06-28 19-026862
99 1 10000 2019-06-09 20:32:00 2019-06-28 19-026905
100 6 10000 2019-08-09 13:54:00 2019-08-28 19-038644
101 4 10000 2019-08-09 22:30:00 2019-08-28 19-038720
102 4 10000 2019-11-09 01:50:00 2019-11-28 19-056263
103 5 10000 2018-09-10 21:49:00 2018-09-28 18-042941
104 1 10000 2018-05-10 20:10:00 2018-05-28 18-020963
105 3 10000 2018-03-10 01:04:00 2018-03-28 18-010757
106 3 10000 2019-07-10 11:28:00 2019-07-28 19-032846
107 1 10000 2018-09-11 22:20:00 2018-09-28 18-043140
108 1 10000 2018-07-11 17:40:00 2018-07-28 18-031935
109 5 10000 2018-06-11 14:41:00 2018-06-28 18-026460
110 6 10000 2018-05-11 16:47:00 2018-05-28 18-021101
111 5 10000 2018-01-11 10:28:00 2018-01-28 18-001737
112 5 10000 2019-02-11 16:00:00 2019-02-28 19-007995
113 5 10000 2019-02-11 20:50:00 2019-02-28 19-006513
114 4 10000 2019-02-11 22:00:00 2019-02-28 19-006512
115 3 10000 2019-04-11 19:40:00 2019-04-28 19-016075
116 5 10000 2019-07-11 00:42:00 2019-07-28 19-032961
117 6 10000 2019-07-11 18:00:00 2019-07-28 19-033107
118 4 10000 2019-08-11 02:00:00 2019-08-28 19-038947
119 6 10000 2019-09-11 23:27:00 2019-09-28 19-045265
120 3 10000 2019-11-11 15:54:00 2019-11-28 19-056681
121 6 10000 2018-12-12 12:53:00 2018-12-28 18-058750
122 6 10000 2018-05-12 21:15:00 2018-05-28 18-021274
123 6 10000 2018-02-12 19:39:00 2018-02-28 18-006829
124 6 10000 2018-01-12 22:47:00 2018-01-28 18-001995
125 2 10000 2018-01-12 10:00:00 2018-01-28 18-001895
126 1 10000 2019-01-12 22:22:00 2019-01-28 19-001896
127 5 10000 2019-03-12 00:45:00 2019-03-28 19-010953
128 4 10000 2019-08-12 17:06:00 2019-08-28 19-039284
129 6 10000 2018-08-13 23:55:00 2018-08-28 18-037897
130 5 10000 2018-06-13 22:34:00 2018-06-28 18-026927
131 6 10000 2019-05-13 20:52:00 2019-05-28 19-021920
132 5 10000 2019-09-13 18:43:00 2019-09-28 19-045605
133 4 10000 2019-10-13 18:46:00 2019-10-28 19-051389
134 6 10000 2018-12-14 03:46:00 2018-12-28 18-058996
135 5 10000 2018-08-14 09:15:00 2018-08-28 18-037957
136 3 10000 2019-06-14 23:50:00 2019-06-28 19-027970
137 6 10000 2018-11-15 19:18:00 2018-11-28 18-054501
138 5 10000 2018-09-15 06:30:00 2018-09-28 18-044019
139 5 10000 2018-07-15 13:32:00 2018-07-28 18-032477
140 1 10000 2018-04-15 01:11:00 2018-04-28 18-016585
141 5 10000 2019-01-15 15:12:00 2019-01-28 19-002278
142 6 10000 2019-06-15 11:10:00 2019-06-28 19-028033
143 1 10000 2019-07-15 02:54:00 2019-07-28 19-033672
144 5 10000 2018-12-16 07:38:00 2018-12-28 18-059333
145 6 10000 2018-09-16 16:10:00 2018-09-28 18-044013
146 5 10000 2018-08-16 04:00:00 2018-08-28 18-038296
147 4 10000 2018-06-16 12:34:00 2018-06-28 18-027359
148 4 10000 2018-06-16 04:30:00 2018-06-28 18-027307
149 5 10000 2019-01-16 23:48:00 2019-01-28 19-002489
150 4 10000 2019-08-16 17:13:00 2019-08-28 19-040096
151 5 10000 2019-08-16 22:21:00 2019-08-28 19-040121
152 4 10000 2019-10-16 21:01:00 2019-10-28 19-052008
153 4 10000 2018-08-17 10:48:00 2018-08-28 18-038533
154 4 10000 2018-06-17 23:55:00 2018-06-28 18-027605
155 4 10000 2018-06-17 00:05:00 2018-06-28 18-027449
156 1 10000 2018-03-17 23:19:00 2018-03-28 18-012030
157 5 10000 2018-03-17 16:10:00 2018-03-28 18-011972
158 3 10000 2018-01-17 23:45:00 2018-01-28 18-002737
159 5 10000 2019-01-17 16:31:00 2019-01-28 19-002622
160 5 10000 2019-03-17 21:40:00 2019-03-28 19-011872
161 6 10000 2019-06-17 03:10:00 2019-06-28 19-028344
162 5 10000 2019-07-17 00:06:00 2019-07-28 19-034095
163 6 10000 2019-07-17 23:00:00 2019-07-28 19-034271
164 5 10000 2019-07-17 23:10:00 2019-07-28 19-034276
165 6 10000 2019-10-17 01:16:00 2019-10-28 19-052025
166 4 10000 2018-08-18 12:58:00 2018-08-28 18-038739
167 5 10000 2018-03-18 09:19:00 2018-03-28 18-012080
168 4 10000 2018-01-18 13:41:00 2018-01-28 18-002826
169 3 10000 2019-03-18 21:08:00 2019-03-28 19-012060
170 5 10000 2019-03-18 23:39:00 2019-03-28 19-012105
171 5 10000 2019-07-18 10:10:00 2019-07-28 19-034345
172 4 10000 2019-08-18 02:15:00 2019-08-28 19-040288
173 1 10000 2019-11-18 00:50:00 2019-11-28 19-057833
174 3 10000 2018-12-19 23:35:00 2018-12-28 18-059926
175 4 10000 2018-12-19 23:30:00 2018-12-28 18-059924
176 6 10000 2018-07-19 18:30:00 2018-07-28 18-033260
177 6 10000 2018-05-19 21:45:00 2018-05-28 18-022532
178 1 10000 2018-05-19 02:40:00 2018-05-28 18-022391
179 5 10000 2018-03-19 10:26:00 2018-03-28 18-012231
180 1 10000 2018-01-19 17:25:00 2018-01-28 18-003034
181 6 10000 2019-03-19 11:07:00 2019-03-28 19-012127
182 6 10000 2019-05-19 20:06:00 2019-05-28 19-023050
183 2 10000 2019-07-19 12:50:00 2019-07-28 19-034583
184 5 10000 2019-07-19 20:31:00 2019-07-28 19-034640
185 3 10000 2019-09-19 20:51:00 2019-09-28 19-046892
186 5 10000 2019-11-19 07:08:00 2019-11-28 19-058060
187 5 10000 2019-11-19 10:41:00 2019-11-28 19-058099
188 4 10000 2019-11-19 19:34:00 2019-11-28 19-058200
189 4 10000 2018-12-20 23:39:00 2018-12-28 18-060116
190 3 10000 2018-07-20 05:02:00 2018-07-28 18-033301
191 5 10000 2018-03-20 19:37:00 2018-03-28 18-012492
192 6 10000 2018-01-20 22:53:00 2018-01-28 18-003218
193 6 10000 2019-05-20 21:38:00 2019-05-28 19-023244
194 6 10000 2019-07-20 15:00:00 2019-07-28 19-034765
195 4 10000 2019-10-20 05:10:00 2019-10-28 19-052582
196 4 10000 2018-11-21 15:24:00 2018-11-28 18-055391
197 6 10000 2018-09-21 09:53:00 2018-09-28 18-044906
198 4 10000 2018-08-21 18:38:00 2018-08-28 18-039325
199 6 10000 2019-01-21 07:00:00 2019-01-28 19-003132
200 5 10000 2019-06-21 13:55:00 2019-06-28 19-029263
201 6 10000 2019-06-21 17:00:00 2019-06-28 19-029441
202 5 10000 2019-07-21 05:09:00 2019-07-28 19-034864
203 4 10000 2019-07-21 12:59:00 2019-07-28 19-034925
204 5 10000 2019-09-21 11:13:00 2019-09-28 19-047170
205 6 10000 2018-12-22 19:35:00 2018-12-28 18-060407
206 4 10000 2018-07-22 19:34:00 2018-07-28 18-033754
207 6 10000 2018-05-22 13:12:00 2018-05-28 18-022949
208 6 10000 2018-04-22 06:00:00 2018-04-28 18-017730
209 1 10000 2018-02-22 23:21:00 2018-02-28 18-008449
210 3 10000 2019-05-22 23:00:00 2019-05-28 19-023675
211 6 10000 2019-06-22 18:32:00 2019-06-28 19-029491
212 4 10000 2019-08-22 07:36:00 2019-08-28 19-041089
213 5 10000 2019-09-22 03:35:00 2019-09-28 19-047288
214 6 10000 2019-09-22 14:51:00 2019-09-28 19-047376
215 5 10000 2019-09-22 23:31:00 2019-09-28 19-047468
216 5 10000 2019-11-22 10:35:00 2019-11-28 19-058671
217 1 10000 2018-12-23 16:45:00 2018-12-28 18-060518
218 6 10000 2018-12-23 12:32:00 2018-12-28 18-060494
219 5 10000 2018-12-23 04:00:00 2018-12-28 18-060452
220 6 10000 2018-09-23 20:38:00 2018-09-28 18-045308
221 5 10000 2018-09-23 12:23:00 2018-09-28 18-045238
222 6 10000 2018-09-23 11:47:00 2018-09-28 18-045235
223 5 10000 2018-09-23 07:22:00 2018-09-28 18-045200
224 6 10000 2018-08-23 10:15:00 2018-08-28 18-039664
225 3 10000 2018-07-23 17:30:00 2018-07-28 18-033958
226 6 10000 2018-07-23 03:01:00 2018-07-28 18-033795
227 4 10000 2018-07-23 00:40:00 2018-07-28 18-033782
228 5 10000 2018-03-23 20:10:00 2018-03-28 18-012955
229 6 10000 2018-01-23 22:40:00 2018-01-28 18-003685
230 4 10000 2018-01-23 13:29:00 2018-01-28 18-003611
231 4 10000 2019-01-23 07:48:00 2019-01-28 19-003372
232 1 10000 2019-01-23 21:12:00 2019-01-28 19-003512
233 1 10000 2019-01-23 22:56:00 2019-01-28 19-003515
234 1 10000 2019-04-23 13:00:00 2019-04-28 19-018137
235 1 10000 2019-06-23 00:20:00 2019-06-28 19-029522
236 4 10000 2019-06-23 15:00:00 2019-06-28 19-029774
237 2 10000 2019-07-23 21:00:00 2019-07-28 19-035553
238 6 10000 2019-07-23 21:55:00 2019-07-28 19-035400
239 5 10000 2019-08-23 20:06:00 2019-08-28 19-041462
240 5 10000 2019-09-23 19:40:00 2019-09-28 19-047675
241 6 10000 2019-09-23 20:45:00 2019-09-28 19-047678
242 5 10000 2019-09-23 21:49:00 2019-09-28 19-047681
243 6 10000 2019-10-23 20:30:00 2019-10-28 19-053304
244 6 10000 2018-12-24 12:22:00 2018-12-28 18-060607
245 5 10000 2018-09-24 04:00:00 2018-09-28 18-045348
246 4 10000 2018-04-24 03:14:00 2018-04-28 18-018038
247 1 10000 2019-03-24 21:15:00 2019-03-28 19-012978
248 6 10000 2019-04-24 03:08:00 2019-04-28 19-018259
249 6 10000 2019-05-24 21:47:00 2019-05-28 19-024031
250 6 10000 2019-06-24 12:00:00 2019-06-28 19-030268
251 6 10000 2019-06-24 22:58:00 2019-06-28 19-029879
252 6 10000 2019-08-24 14:25:00 2019-08-28 19-041580
253 4 10000 2019-08-24 14:46:00 2019-08-28 19-041611
254 1 10000 2019-11-24 23:23:00 2019-11-28 19-059088
255 4 10000 2018-09-25 22:03:00 2018-09-28 18-045713
256 6 10000 2018-09-25 12:29:00 2018-09-28 18-045634
257 4 10000 2018-06-25 21:36:00 2018-06-28 18-029092
258 4 10000 2018-06-25 17:15:00 2018-06-28 18-029066
259 6 10000 2018-06-25 10:39:00 2018-06-28 18-029029
260 4 10000 2018-05-25 01:15:00 2018-05-28 18-023389
261 6 10000 2018-01-25 10:00:00 2018-01-28 18-004168
262 6 10000 2019-01-25 22:07:00 2019-01-28 19-003800
263 1 10000 2019-03-25 23:32:00 2019-03-28 19-013127
264 3 10000 2019-04-25 05:00:00 2019-04-28 19-018478
265 5 10000 2019-06-25 03:13:00 2019-06-28 19-029897
266 1 10000 2019-08-25 05:56:00 2019-08-28 19-041690
267 3 10000 2019-11-25 18:38:00 2019-11-28 19-059224
268 3 10000 2018-12-26 21:56:00 2018-12-28 18-060906
269 6 10000 2018-09-26 19:35:00 2018-09-28 18-045926
270 6 10000 2018-09-26 10:30:00 2018-09-28 18-045789
271 6 10000 2018-04-26 11:45:00 2018-04-28 18-018445
272 6 10000 2018-02-26 00:25:00 2018-02-28 18-008902
273 2 10000 2019-09-26 19:02:00 2019-09-28 19-048273
274 6 10000 2019-11-26 10:32:00 2019-11-28 19-059309
275 2 10000 2018-11-27 22:00:00 2018-11-28 18-056435
276 3 10000 2018-07-27 22:36:00 2018-07-28 18-034695
277 2 10000 2018-04-27 23:00:00 2018-04-28 18-018711
278 5 10000 2018-02-27 16:27:00 2018-02-28 18-009211
279 3 10000 2018-01-27 14:12:00 2018-01-28 18-004367
280 2 10000 2018-01-27 13:00:00 2018-01-28 18-004387
281 5 10000 2018-01-27 01:22:00 2018-01-28 18-004265
282 6 10000 2019-01-27 15:01:00 2019-01-28 19-004046
283 1 10000 2019-01-27 21:00:00 2019-01-28 19-004099
284 4 10000 2019-04-27 05:17:00 2019-04-28 19-018891
285 4 10000 2019-05-27 01:08:00 2019-05-28 19-024375
286 6 10000 2019-05-27 23:15:00 2019-05-28 19-024517
287 6 10000 2019-09-27 18:50:00 2019-09-28 19-048489
288 6 10000 2019-11-27 16:00:00 2019-11-28 19-059586
289 6 10000 2018-12-28 01:15:00 2018-12-28 18-061082
290 5 10000 2018-09-28 11:30:00 2018-09-28 18-046177
291 5 10000 2018-07-28 22:15:00 2018-07-28 18-034867
292 5 10000 2018-04-28 03:20:00 2018-04-28 18-018735
293 6 10000 2019-01-28 12:40:00 2019-01-28 19-004172
294 1 10000 2019-09-28 16:20:00 2019-09-28 19-048627
295 1 10000 2019-10-28 14:51:00 2019-10-28 19-054188
296 5 10000 2019-11-28 00:25:00 2019-11-28 19-059621
297 6 10000 2019-11-28 13:09:00 2019-11-28 19-059669
298 5 10000 2018-11-29 20:49:00 2018-11-28 18-056624
299 4 10000 2018-07-29 00:56:00 2018-07-28 18-034886
300 3 10000 2018-05-29 00:11:00 2018-05-28 18-023978
301 4 10000 2018-04-29 20:00:00 2018-04-28 18-018976
302 5 10000 2018-04-29 17:00:00 2018-04-28 18-018954
303 2 10000 2018-01-29 00:55:00 2018-01-28 18-004573
304 6 10000 2019-04-29 07:05:00 2019-04-28 19-019206
305 5 10000 2019-04-29 16:10:00 2019-04-28 19-019314
306 6 10000 2019-05-29 06:10:00 2019-05-28 19-024763
307 6 10000 2019-05-29 12:00:00 2019-05-28 19-025192
308 4 10000 2019-05-29 12:17:00 2019-05-28 19-024821
309 1 10000 2019-06-29 21:00:00 2019-06-28 19-030913
310 3 10000 2019-08-29 11:58:00 2019-08-28 19-042610
311 1 10000 2018-09-30 20:27:00 2018-09-28 18-046560
312 5 10000 2018-09-30 15:33:00 2018-09-28 18-046536
313 3 10000 2018-09-30 12:09:00 2018-09-28 18-046508
314 5 10000 2018-07-30 20:48:00 2018-07-28 18-035236
315 6 10000 2018-07-30 05:20:00 2018-07-28 18-035065
316 6 10000 2018-06-30 18:44:00 2018-06-28 18-029981
317 6 10000 2018-01-30 10:40:00 2018-01-28 18-004812
318 6 10000 2019-04-30 23:45:00 2019-04-28 19-019547
319 5 10000 2019-05-30 12:47:00 2019-05-28 19-025009
320 5 10000 2019-05-30 20:56:00 2019-05-28 19-025076
321 5 10000 2019-05-30 21:58:00 2019-05-28 19-025102
322 3 10000 2019-07-30 00:01:00 2019-07-28 19-036579
323 3 10000 2018-05-31 01:30:00 2018-05-28 18-024433
324 6 10000 2018-01-31 11:15:00 2018-01-28 18-004969
325 6 10000 2019-05-31 12:59:00 2019-05-28 19-025205
326 5 10000 2019-08-31 04:34:00 2019-08-28 19-042948
327 3 10000 <NA> 2018-10-28 18-046912
328 6 10000 <NA> 2018-10-28 18-046933
329 5 10000 <NA> 2018-10-28 18-046944
330 3 10000 <NA> 2018-10-28 18-047230
331 1 10000 <NA> 2018-10-28 18-047573
332 5 10000 <NA> 2018-10-28 18-047961
333 5 10000 <NA> 2018-10-28 18-047980
334 5 10000 <NA> 2018-10-28 18-048002
335 5 10000 <NA> 2018-10-28 18-048475
336 3 10000 <NA> 2018-10-28 18-048273
337 4 10000 <NA> 2018-10-28 18-048718
338 6 10000 <NA> 2018-10-28 18-048841
339 6 10000 <NA> 2018-10-28 18-049140
340 5 10000 <NA> 2018-10-28 18-049640
341 2 10000 <NA> 2018-10-28 18-051495
342 6 10000 <NA> 2018-10-28 18-051536
343 6 10000 <NA> 2018-10-28 18-051586
344 5 10000 <NA> 2018-10-28 18-051996
NHD_NAME
1 Holly Hills
2 Mount Pleasant
3 Carondelet
4 Fountain Park
5 O'Fallon
6 Old North St. Louis
7 Fairground Neighborhood
8 Baden
9 The Ville
10 Wells Goodfellow
11 Jeff Vanderlou
12 Hyde Park
13 The Ville
14 St. Louis Hills
15 Wells Goodfellow
16 Wells Goodfellow
17 Greater Ville
18 Wells Goodfellow
19 West End
20 Downtown West
21 O'Fallon
22 Mount Pleasant
23 Baden
24 Gravois Park
25 North Hampton
26 Gravois Park
27 Academy
28 Carondelet
29 Hamilton Heights
30 Near North Riverfront
31 The Ville
32 Baden
33 Hamilton Heights
34 Mount Pleasant
35 St. Louis Place
36 Mark Twain I-70 Industrial
37 Jeff Vanderlou
38 Baden
39 Tower Grove South
40 Bevo Mill
41 Wells Goodfellow
42 Wells Goodfellow
43 Visitation Park
44 Greater Ville
45 Jeff Vanderlou
46 Walnut Park West
47 Tower Grove South
48 Wells Goodfellow
49 Mark Twain
50 Peabody Darst Webbe
51 O'Fallon
52 Penrose
53 St. Louis Place
54 Mark Twain
55 Gravois Park
56 Wells Goodfellow
57 Carr Square
58 Greater Ville
59 Walnut Park East
60 Dutchtown
61 Walnut Park West
62 Walnut Park East
63 Patch
64 Baden
65 Greater Ville
66 Marine Villa
67 Hyde Park
68 College Hill
69 Baden
70 Covenant Blu-Grand Center
71 Wells Goodfellow
72 Baden
73 Walnut Park East
74 Baden
75 Gravois Park
76 Penrose
77 Walnut Park East
78 Penrose
79 Academy
80 Greater Ville
81 Jeff Vanderlou
82 Mount Pleasant
83 Kingsway East
84 Dutchtown
85 Greater Ville
86 Penrose
87 O'Fallon
88 Tower Grove South
89 Dutchtown
90 Fairground Neighborhood
91 Vandeventer
92 Peabody Darst Webbe
93 Covenant Blu-Grand Center
94 Walnut Park East
95 North Riverfront
96 Lewis Place
97 Marine Villa
98 Jeff Vanderlou
99 Mount Pleasant
100 Walnut Park West
101 Carr Square
102 Jeff Vanderlou
103 Hamilton Heights
104 Mount Pleasant
105 Dutchtown
106 Dutchtown
107 Carondelet
108 Patch
109 The Ville
110 Greater Ville
111 Central West End
112 Fountain Park
113 Greater Ville
114 Jeff Vanderlou
115 Benton Park West
116 Lewis Place
117 Greater Ville
118 Hyde Park
119 Walnut Park West
120 Dutchtown
121 Walnut Park West
122 Kingsway East
123 College Hill
124 North Pointe
125 Tower Grove South
126 Bevo Mill
127 Vandeventer
128 Hyde Park
129 Penrose
130 Wells Goodfellow
131 Penrose
132 Greater Ville
133 Midtown
134 Penrose Park
135 Wells Goodfellow
136 Dutchtown
137 O'Fallon
138 Kingsway East
139 Wells Goodfellow
140 Bevo Mill
141 Wells Goodfellow
142 Penrose
143 Mount Pleasant
144 Kingsway East
145 Walnut Park West
146 Academy
147 Downtown
148 College Hill
149 The Ville
150 Old North St. Louis
151 Kingsway West
152 Columbus Square
153 Downtown
154 Midtown
155 Hyde Park
156 Mount Pleasant
157 Lewis Place
158 Tower Grove East
159 Wells Goodfellow
160 Lewis Place
161 Baden
162 Fountain Park
163 Baden
164 West End
165 Baden
166 Jeff Vanderlou
167 Greater Ville
168 Covenant Blu-Grand Center
169 Gravois Park
170 Fountain Park
171 Lewis Place
172 Carr Square
173 Dutchtown
174 Soulard
175 St. Louis Place
176 Walnut Park West
177 Penrose
178 Carondelet
179 West End
180 Boulevard Heights
181 Walnut Park West
182 Riverview
183 Hi-Pointe
184 Lewis Place
185 Peabody Darst Webbe
186 Hamilton Heights
187 Hamilton Heights
188 Jeff Vanderlou
189 Old North St. Louis
190 Benton Park West
191 Hamilton Heights
192 Mark Twain
193 Mark Twain
194 Penrose
195 Old North St. Louis
196 Downtown West
197 Greater Ville
198 Midtown
199 Walnut Park East
200 Wells Goodfellow
201 Penrose
202 Kingsway West
203 Jeff Vanderlou
204 Wells Goodfellow
205 Fairground Neighborhood
206 Jeff Vanderlou
207 College Hill
208 Walnut Park East
209 Bevo Mill
210 Tower Grove East
211 Kingsway East
212 Hyde Park
213 Wells Goodfellow
214 Fairground Neighborhood
215 Wells Goodfellow
216 Greater Ville
217 Carondelet
218 Greater Ville
219 Fountain Park
220 Baden
221 The Ville
222 Kingsway East
223 Vandeventer
224 Penrose
225 Dutchtown
226 Greater Ville
227 Jeff Vanderlou
228 Greater Ville
229 Walnut Park East
230 Downtown West
231 Jeff Vanderlou
232 Carondelet
233 Dutchtown
234 Patch
235 Dutchtown
236 College Hill
237 Southwest Garden
238 North Pointe
239 Academy
240 Kingsway East
241 Baden
242 Hamilton Heights
243 Walnut Park West
244 O'Fallon
245 Fountain Park
246 Fairground Neighborhood
247 Carondelet
248 Baden
249 Walnut Park West
250 Baden
251 Kingsway East
252 Walnut Park East
253 Downtown West
254 Carondelet
255 Old North St. Louis
256 Walnut Park East
257 St. Louis Place
258 Columbus Square
259 Penrose
260 Old North St. Louis
261 O'Fallon
262 College Hill
263 Mount Pleasant
264 Dutchtown
265 Kingsway East
266 Patch
267 Gravois Park
268 The Gate District
269 Baden
270 Baden
271 Mark Twain I-70 Industrial
272 Penrose
273 Forest Park South East
274 Greater Ville
275 Shaw
276 Gravois Park
277 North Hampton
278 Vandeventer
279 Dutchtown
280 Tower Grove South
281 Wells Goodfellow
282 Walnut Park East
283 Dutchtown
284 St. Louis Place
285 Downtown
286 North Pointe
287 Penrose
288 Baden
289 Mark Twain
290 Wells Goodfellow
291 Kingsway West
292 Wells Goodfellow
293 Walnut Park East
294 Mount Pleasant
295 Carondelet
296 Hamilton Heights
297 North Riverfront
298 Hamilton Heights
299 Jeff Vanderlou
300 Soulard
301 Downtown
302 Hamilton Heights
303 Forest Park South East
304 College Hill
305 Wells Goodfellow
306 Walnut Park East
307 Penrose
308 Carr Square
309 Patch
310 Dutchtown
311 Dutchtown
312 Wells Goodfellow
313 Gravois Park
314 Academy
315 O'Fallon
316 Baden
317 Mark Twain
318 Mark Twain I-70 Industrial
319 Lewis Place
320 Lewis Place
321 Wells Goodfellow
322 Peabody Darst Webbe
323 Gravois Park
324 Greater Ville
325 O'Fallon
326 Wells Goodfellow
327 Benton Park
328 Fairground Neighborhood
329 The Ville
330 Tower Grove East
331 Carondelet
332 Wells Goodfellow
333 Hamilton Heights
334 Hamilton Heights
335 West End
336 Dutchtown
337 Downtown
338 Walnut Park East
339 Walnut Park West
340 Fountain Park
341 Tower Grove South
342 Baden
343 Baden
344 Academy
These records are in much better shape.
They have both X and Y coordinates.
Function transforms all the State Plane Coordinate values into NAD84 lat/long coordinates.
More modern mapping structure used for GPS Mapping.
Used censusxy library to pull latitude/longitude.
The geocode function from the library requires a street address and number, city, and zip code (if available).
It goes to the US Census Bureau to look up the address reported on police record and returns a lat/long.
It creates an sf file and allows plotting of locations on a map.
Can only convert 22 instances with censusxy since some addresses locations are missing.
Add neighborhoods.
From https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html
***
Peaks illustrate highest crime numbers for that area.
Contours indicate similiar occurrances.
***
It uses clusters counts to illustrate homicice numbers in selected city areas.
As you drill down it recalculates the numbers over city areas.
From intersection of Goodfellow and MLK.
North along Goodfellow to W. Florissant.
Then Southeast along W. Florissant to Prarie.
Then southwest along Prarie/Vandeventner to MLK.
Back to MLK and Goodfellow.
This is how it plots out with homicides.
A better prediction here, but the box still misses the south side hotspot.
Also, note the area running west along Interstate 55 and Northwest along Interstate 70.
And the mayor said she would give him an A?
These are the 6 police districts.
Now they are considering restructuring them again.
They want to increase the number.
Improvement or just more overhead?
Need to collect more data for greater understanding of crime parameters.
This data set has close to 8,000 instances of “FIREARM” defined crime. Where are the locations?
Need to plot heroine and cocaine locations to see overlaps.
There is no gang data available since 2012. St Louis does not have a Gang Division. Does it need one?
UCR reporting structure is poorly constructed for nation as a whole. Data is inconsistent and ill defined.
---
title: "Homicides"
output:
flexdashboard::flex_dashboard:
storyboard: true
source_code: embed
theme: cerulean
---
```{r, echo=FALSE, message=FALSE, warning=FALSE}
knitr::opts_chunk$set(echo = FALSE,
include = FALSE,
eval = TRUE,
message = FALSE,
warning = FALSE,
fig.retina = 1,
tidy = TRUE)
```
```{r echo=FALSE}
# install all the library packages
library(rgdal)
library(sp)
library(sf)
library(raster)
library(leaflet)
library(leafpop)
library(mapview)
library(tidyverse)
library(censusxy)
library(tidycensus)
library(ggplot2)
library(ggmap)
library(plotly)
library(RColorBrewer)
library(data.table)
library(fasttime)
library(sparklyr)
library(lubridate)
library(maps)
library(stringr)
library(readr)
library(knitr)
```
### 1. Begin by collecting crime data from the STL Metropolitan Police Website
```{r, include=TRUE}
# Collect St Louis City crime UCR statistics
# pull in state coordinate system files from st louis police reports using data.table
crime <- fread("Group2018.csv", stringsAsFactors=FALSE)
head(crime)
```
***
- The STL Metropolitan Police produces a monthly crime update.
- Stored in a csv format and can be downloaded.
- Located at .
- The file provides all crime details collected from the preceding month.
- Contains locations, neighborhoods, precincts, map coordinates and times of crimes in the St Louis Metropolitan Area.
### 2. Look at the Data Values
```{r, include=TRUE}
summary(crime)
```
***
- Again, some fields are irrelevant to our analysis.
- We will remove these elements using a tidyverse library called *dplyr*.
- We will also have to restructure certain date/time variables.
- Flags are not needed.
- Don't see how count field is significant in the analysis.
### 3. Adjust Data Structures to Match that Needed for Analysis
```{r, include=TRUE}
crimeA <- crime %>%
dplyr::select(-FlagCrime, -FlagUnfounded, -FlagAdministrative, -Count, -FlagCleanup) %>%
filter(Crime == 10000) %>%
distinct(Complaint, .keep_all = TRUE)
glimpse(crimeA)
```
***
- I wanted to select a specific crime. In this case we will look at Homicides.
- Some data fields are not relevant to the analysis so I've limited the data to the following 6 elements.
- Homicides are UCR coded as *10000*.
- Although the STLMPD website states rows are unique, they are *NOT*.
- During this phase I also wanted to determine data types.
- The mix is a combination of characters string and integers.
- I will have to re-charactize some elements to more easily manipulate later.
- "CodedMonth" and "DateOccur" are not date/time elements, so they need to be changed.
### 4. Prepare Data for Manipulating Date/time Fields
```{r, include=FALSE}
crimeA$CodedMonth <- str_c(crimeA$CodedMonth, "28", sep = "-") # use stringr to create add a day to the y/m structure
crimeA$CodedMonth <- as_date(crimeA$CodedMonth) # use lubridate to convert to actual y/m/d
crimeA$DateOccur <- mdy_hm(crimeA$DateOccur) # use lubridate to change string to date/time structure
```
```{r, include=TRUE}
### Result of Changing String Value {data-background=#fae5e3}
# - "CodedMonth" is now a date format and "DateOccur" is now a POSIX date time data type.
# - Check structures of the data.
str(crimeA)
```
***
- Need to use some R libraries to convert data types.
- Used *stringr* and *lubridate* libraries to change data types.
- Changed "CodedMonth" to a string value closer to one resembling a year/month/day field.
- Used 28 days as the day value so I do not have to constantly worry about the changing days/month values.
- Since the data is collected as of the last day of the month, it will not affect the monthly crime perspective.
- Next I created a concatonated string group and convert that field into a "POSIX" day/month/day variable.
```{r}
### Check Final Data Structure {data-background=#fae5e3}
summary(crimeA)
```
```{r}
### Make Date Structures Compatable and Calculate Reporting Delays {data-background=#fae5e3}
# - An interesting side note is to see the differences between reporting day and actual incident date.
# - Some of the records are reported significantly longer than 30 days.
crimeB <- crimeA %>% mutate(Reporting.diff = CodedMonth - as_date(DateOccur)) %>%
dplyr::select(Reporting.diff:Complaint) %>%
arrange(desc(Reporting.diff))
crimeB$Neighborhood <- as_factor(crimeB$Neighborhood) # change to factor for later join
```
### 5. Review Reporting Delays
```{r, include=TRUE}
crimeB
```
### 6. Bring in the Neighborhood Details
```{r, include=TRUE}
### Now join neighborhoods with names
#add neighborhood shapes to a data frame
# From https://www.census.gov/geo/maps-data/data/cbf/cbf_state.html
hoods.sf <- readOGR("St Louis Shape files/nbrhds_wards/BND_Nhd88_cw.shp")
hoods.sf <- spTransform(hoods.sf, CRS("+proj=longlat +datum=WGS84"))
hoods <- mapview(hoods.sf, map.types = c("OpenStreetMap"),
layer.name = c("Neighborhoods"),
alpha.regions = 0.1,
alpha = 2,
legend = FALSE,
zcol = c("NHD_NAME"))
hoods
```
***
- Collected US Census data to bring in geospatial polygons that represent St Louis Neighborhoods.
- Transformed mapview data into *WGS84* structure.
- Check to make sure data is a geospatial object.
- Use census geospatial data to generate a map.
```{r}
### Convert Neighborhood Details {data-background=#fae5e3}
# - Change SF file into a data frame.
# collect neighborhood details from shape file
hoods.df <- as(hoods.sf, "data.frame")
class(hoods.df) # check class
```
### 7. Look at the data frame after adding in Neighborhood data
```{r, include=TRUE}
glimpse(hoods.df)
```
***
- We have 88 neighborhoods and their name and number are factor types in R.
- The polygon shapes are included in this data frame.
```{r}
### Clean Up Data - Trim Neighborhoods and Prepare for Joins {data-background=#fae5e3}
# - Bring in the neighborhood name with their respective number codes.
# - Create a new data frame.
crimeC <- hoods.df %>% dplyr::select(NHD_NUM, NHD_NAME)
# crimeC$NHD_NUM <- as.integer(crimeC$NHD_NUM) # convert to integer
# join carkacks table with hoods table to get neighborhood names
crimeD <- left_join(crimeB, crimeC, by = c("Neighborhood" = "NHD_NUM"))
```
```{r}
### See the Final Data Frame
glimpse(crimeD)
```
### 8. Group by Month and Count Number of Homicides per Month
```{r, include=TRUE}
crimeA %>%
group_by(CodedMonth) %>%
count(Crime) %>%
arrange(desc(n))
```
***
- Group data by coded month.
- Count the number of *homicides per month*.
- Data presented in a bar graph with totals displayed above the bar.
- I added a smoothing line to get a better view of the crime movement.
- Note that October 2018 was the peak.
- It was when Channel 5 reported the sever increase in carjackings. Looks like homicids too.
- It was also the timeframe when they reported establishing atask force.
```{r, include=FALSE}
### Plot the count by month
crime.month <- crimeA %>%
group_by(CodedMonth) %>%
count(Crime) %>%
arrange(desc(n))
xx = ggplot(crime.month, aes(x = CodedMonth, y = n)) +
geom_text(aes(label = n, y = n), size = 5, position = position_stack(vjust = 1.2)) +
geom_col(color = "cornflowerblue") +
geom_point() +
stat_smooth() + # add a smoothing regerssion for time series
scale_x_date(date_breaks = "4 weeks", date_labels = "%m") +
theme(axis.text.x = element_text(angle = 90)) + # change tex to verticle
labs(title = "Homicides Per Month", x= "Month", y = "C
Homicide Count")
```
### 9. Plot Homicides per Month Using _ggplot2_ Library
```{r, include=TRUE}
### Homicides by Month
xx
```
***
### 10. Look at Neighborhood's by Name and Count Numbers {data-background=#fae5e3}
```{r, include=TRUE}
### Neighborhood By Name
### Group by Neighborhood and count
crimeD %>%
mutate_if(is.factor,
fct_explicit_na,
na_level = "to_impute") %>%
group_by(NHD_NAME) %>%
count(Crime, sort = TRUE) %>%
arrange(desc(n)) %>%
ungroup()%>%
mutate (cumulative = cumsum(n), total = sum(n), cumul.percent = cumsum(c(n/total *100)))
```
***
- Had to adjust the factor variables (NHD_NAME) and to account for missing variables (NA).
- Count by crime and put in decending order.
- This is a display of the highest crime neighborhoods.
- 70% of the homicides are committed in the top 21 neighborhoods (23%)
```{r}
### 11. Neighborhoods Count by Month
# - Group by Neighborhood Name.
# - Chart puts data in a descending order and presents greater than 5.
### Plot the count by month
hood.number <- crimeD %>%
mutate_if(is.factor,
fct_explicit_na,
na_level = "to_impute") %>%
group_by(NHD_NAME) %>%
count(Crime) %>%
filter(n > 5) %>%
arrange(desc(n))
```
```{r}
xy = ggplot(hood.number, aes(x = reorder(NHD_NAME, +n), y = n)) +
geom_bar(stat = "identity") +
geom_col(color = "cornflowerblue") +
coord_flip() +
theme(axis.text.x = element_text(angle = 90)) + # change tex to verticle
labs(title = "Homicides by Neighborhood", x= "Neighborhood", y = "Homicide Count")
```
### 11. Neighborhoods Count by Month
```{r, include=TRUE}
xy
```
***
- Group by Neighborhood Name.
- Chart puts data in a descending order and presents greater than 5.
```{r}
### Plot the count by month
crime.tod <- crimeD %>%
select(Crime, CodedMonth, DateOccur) %>%
mutate(hour = hour(DateOccur)) %>%
mutate(am = am(DateOccur)) %>% # determine if the date occurred in the AM or PM
group_by(CodedMonth, am) %>%
count(Crime)
```
### 12. Time of Day Carjacks
```{r include=TRUE}
ggplot(crime.tod, aes(x = CodedMonth, y = n, fill = am)) +
scale_fill_discrete(name = "Timeframe", labels = c("PM", "AM")) +
geom_col(color = "cornflowerblue") +
scale_x_date(date_breaks = "5 weeks", date_labels = "%m") +
theme(axis.text.x = element_text(angle = 90)) +
labs(title = "Monthly Homicide Timeframe", x= "Am or PM", y = "Homicides Count") +
labs(fill = "Mornings")
```
***
- Looks like there is a significant difference in times that the crime occured.
- Seems to be more afternoon to midnight occurrances than post midnight to noon.
- May need a further breakdown of crime times to make this meaningful.
### 13. Let's Look at the Geospatial Aspects of the Homicide Analysis
```{r, include=TRUE}
### Summary of the Characteristics of the Crime Data {data-background=#fae5e3}
summary(crimeD)
```
***
- We will use the data we restructed earlier in the analysis.
- We will use the crime D file.
- Check the structure of the file we selected.
### 14. Important to understanding the geospatial structures of the data
- XCoord and YCoord coordinates are based on the State Plane North American Datum 1983 (NAD83) format.
- This data will have to be converted to lat/long values.
- Some of the XCoords and YCoords have values of O. This will need to be accounted for later in the analysis.
```{r}
### Let's Review the Basic Data Structure {data-background=#fae5e3}
str(crimeD)
```
### 18. Must Account For Inconsistent Coordinate Data
```{r}
crimeD.zeros <- crimeD %>% filter(XCoord < 1)
```
```{r, include=TRUE}
### Missing Coordinates {data-background=#fae5e3}
crimeD.zeros # there are 20 homicide records that cannot be processed directly
```
***
- Collect those records whose X/Y values are zeros.
- These records will need a different type of processing.
```{r}
### Records That Can Be Directly Converted to Lat/Long {data-background=#fae5e3}
crimeD.complete <- crimeD %>% filter(XCoord > 1)
```
### 19. Complete Records
```{r, include=TRUE}
crimeD.complete
```
***
- These records are in much better shape.
- They have both X and Y coordinates.
### 20. Now we need to convert the NAD83 Coordinates to WGS84 Structure
```{r, echo=TRUE}
nad83_coords <- data.frame(x=crimeD.complete$XCoord, y=crimeD.complete$YCoord) # My coordinates in NAD83
nad83_coords <- nad83_coords *.3048 ### Feet to meters
coordinates(nad83_coords) <- c('x', 'y')
proj4string(nad83_coords)=CRS("+init=epsg:2815")
coordinates_deg <- spTransform(nad83_coords,CRS("+init=epsg:4326"))
coordinates_deg
#str(coordinates_deg)
#class(coordinates_deg)
# add converted lat-lonf and convert to numeric values
crimeD.complete$lon <- as.numeric(coordinates_deg$x)
crimeD.complete$lat <- as.numeric(coordinates_deg$y)
#class(crimeD.complete)
```
***
- Function transforms all the State Plane Coordinate values into NAD84 lat/long coordinates.
- More modern mapping structure used for GPS Mapping.
```{r}
### Review Charistics of Downloaded Crime Data {data-background=#fae5e3}
glimpse(crimeD.complete)
```
### 21. Get Incomplete Data Missing Coordinates {data-background=#fae5e3}
- Used _censusxy_ library to pull latitude/longitude.
- The geocode function from the library requires a street address and number, city, and zip code (if available).
- It goes to the US Census Bureau to look up the address reported on police record and returns a lat/long.
- It creates an _sf_ file and allows plotting of locations on a map.
- Can only convert 22 instances with _censusxy_ since some addresses locations are missing.
```{r}
data <- mutate(crimeD.zeros, address.comb = paste(CADAddress, CADStreet, sep = " "), city = "St Louis", state = "MO")
crimeD_sf <- cxy_geocode(data, address = address.comb, city = city, state = state, style = "minimal", output = "sf")
STL_homicides.small <- mapview(crimeD_sf,
map.types = c("OpenStreetMap"),
legend = FALSE,
popup = popupTable(data,zcol = c("Complaint",
"CodedMonth",
"NHD_NAME",
"District",
"Crime",
"Description")))
```
```{r}
### Locations Obtained From US Census With Addresses Only ...
STL_homicides.small
```
```{r}
### Larger Grouping that Contained Coordinates
#- These records contain the X/Y plotted locations.
### create an sf file that will map coordinates
data.one <- mutate(crimeD.complete, address.comb = paste(CADAddress, CADStreet, sep = " "), city = "St Louis", state = "MO")
crimeD_one.sf <- st_as_sf(data.one, coords = c("lon", "lat"), crs = 4326, agr = "constant")
STL_homicides <- mapview(crimeD_one.sf, map.types = c("OpenStreetMap"),
legend = FALSE,
popup = popupTable(data.one, zcol = c("Complaint",
"CodedMonth",
"NHD_NAME",
"District",
"Crime",
"Description")))
```
### 22. Combine Map Sets to View the Entire Picture of Homicide Location in St Louis
```{r, include=TRUE}
total_homicides <- STL_homicides + STL_homicides.small
total_homicides
```
```{r}
### Bring Up Neighborhood Map {data-background=#fae5e3}
hoods
```
***
- Add neighborhoods.
- From
### 24. Final Map of Homicides with Neighborhood Overlays
```{r, include=TRUE}
#- Combine all the maps.
total_homicides <- STL_homicides + STL_homicides.small + hoods
total_homicides
```
***
- These records are overlaid on the neighborhood polygons.
- They have both X and Y coordinates.
```{r, echo=FALSE}
### Now We Look at Some Plots Targeting the Intensity of the Crime Area {data-background=#fae5e3}
# - Start with a quick plot of the homicides locations.
### reduce crime to violent crimes in downtown
violent_crimes <- crimeD.complete %>%
filter(
Crime == 10000,
-90.3238 <= lon & lon <= -90.1794334,
38.0 <= lat & lat <= 39.0 )
# use qmplot to make a scatterplot on a map
qmplot(lon, lat, data = violent_crimes,
maptype = "toner-lite", color = I("red"), zoom = 12)
```
### 25. Now We Look at These Homicides Plots with Density Contours
```{r, include=TRUE}
### Density contour plots
qmplot(lon, lat, data = violent_crimes, maptype = "toner-lite",
geom = "density2d", color = I("red"), zoom = 12)
```
***
- Peaks illustrate highest crime numbers for that area.
- Contours indicate similiar occurrances.
### 26. Another View Using Same Data Set Gives Us Heat Map
```{r, include=TRUE, fig.width=7}
### This provides a good look at the density of homicides in the city
qmplot(lon, lat, data = violent_crimes, geom = "blank",
zoom = 14, maptype = "toner-background", legend = FALSE) +
stat_density_2d(aes(fill = ..level..), geom = "polygon", alpha = .35, colour = NA) +
scale_fill_gradient2("Homicides\nHeatmap", low = "white", mid = "yellow", high = "red", midpoint = 20)
```
***
- Darker areas indicate higher level of homicides.
```{r}
### Another View of Crime Area Numbers {data-background=#fae5e3}
# - Use clusters to illustrate numbers in an area
zz <- leaflet(data=crimeD.complete) %>%
addTiles() %>%
setView(-90.222, 38.608, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(lng = ~lon,
lat = ~lat,
fillColor = blues9,
stroke = FALSE, fillOpacity = 0.8,
clusterOptions = markerClusterOptions(),
popup = ~DateOccur) %>%
addPolygons(data= hoods.sf, label = ~NHD_NAME,
color = "#444444",
weight = 1,
smoothFactor = 0.5,
opacity = 1.0,
fillOpacity = 0.005,
highlightOptions = highlightOptions(color = "white",
weight = 2,
bringToFront = TRUE))
```
### 27. Here is a Very Interesting View Called a Cluster Map
```{r, include=TRUE}
zz
```
***
- It uses clusters counts to illustrate homicice numbers in selected city areas.
- As you drill down it recalculates the numbers over city areas.
```{r}
#### Task force focus
### Created database that defines the crime focus area
police_crime_focus <- fread("police_crime_focus.csv", stringsAsFactors=FALSE)
### Create a spatial file of the police crime focus
# police_crime_focus
police_point.sf <- st_as_sf(police_crime_focus,
coords = c("lon", "lat"),
crs = 4326, agr = "constant")
###police points
police_point.sf
### Create matrisx of lat/long
df <- data.frame(police_crime_focus$lon, police_crime_focus$lat)
# You need first to close your polygon
# (first and last points must be identical)
df <- rbind(df, df[1,])
### Create a lolygon of the area of the police box
police.polygon <- st_sf(st_sfc(st_polygon(list(as.matrix(df)))), crs = 4326)
# police.polygon
police.box <- mapview(police.polygon, map.types = c("OpenStreetMap"),
layer.name = c("Police Box"),
legend = FALSE,
alpha.regions = 0.3,
alpha = 6,
label = NULL,
color = "red",
col.regions = "red")
## Show police box in red
```
### 28. This Illustrates the "Hayden Rectangle" Plotted Out
```{r, include=TRUE}
police.box
```
***
- From intersection of Goodfellow and MLK.
- North along Goodfellow to W. Florissant.
- Then Southeast along W. Florissant to Prarie.
- Then southwest along Prarie/Vandeventner to MLK.
- Back to MLK and Goodfellow.
```{r}
# Add in Police Box
STLtotal_homicides <- STL_homicides + STL_homicides.small + police.box
```
### 29. This is the Chief's Box Overlaid with Homicides
```{r, include=TRUE}
STLtotal_homicides
```
***
- This is how it plots out with homicides.
- A better prediction here, but the box still misses the south side hotspot.
- Also, note the area running west along Interstate 55 and Northwest along Interstate 70.
- And the mayor said she would give him an *A*?
```{r}
mapshot(total_homicides, url = paste0(getwd(), "/homicide_map.html"),
file = paste0(getwd(), "/carjack_map.png"))
```
```{r}
mapshot(zz , url = paste0(getwd(), "/cluster_homicides.html"),
file = paste0(getwd(), "/cluster_homicides.png"))
```
```{r}
mapshot(STLtotal_homicides , url = paste0(getwd(), "/homicides_police_box.html"),
file = paste0(getwd(), "/homicides_police_box.png"))
```
```{r, message=FALSE}
#add police district shapes to a data frame
police_district.sf <- readOGR("police-districts/GIS.STL.POLICE_DISTRICTS_2014.shp")
police_district.sf <- spTransform(police_district.sf, CRS("+proj=longlat +datum=WGS84"))
police_district <- mapview(police_district.sf, map.types = c("OpenStreetMap"),
layer.name = c("DISTNO"),
alpha.regions = 0.1,
alpha = 7,
legend = FALSE,
zcol = c("DISTNO"))
```
### 30. View Crime based on Police Districts
```{r, include=TRUE}
police_district
```
***
- Established in 2014.
- These are the 6 police districts.
- Now they are considering restructuring them again.
- They want to increase the number.
- Improvement or just more overhead?
```{r}
# combine total crimes and pokice districts
district_homicides <- police_district + STL_homicides + STL_homicides.small
```
### 31. This Overlays Homicides Within the Police Districts
```{r, include=TRUE}
district_homicides
```
```{r, echo=FALSE}
# Provide cluster view with current police districts using
xxx <- leaflet(data=crimeD.complete) %>%
addTiles() %>%
setView(-90.222, 38.608, zoom = 11) %>%
addProviderTiles(providers$CartoDB.Positron) %>%
addCircleMarkers(lng = ~lon,
lat = ~lat,
fillColor = blues9,
stroke = FALSE, fillOpacity = 0.8,
clusterOptions = markerClusterOptions(),
popup = ~DateOccur) %>%
addPolygons(data=police_district.sf, label = ~DISTNO,
color = "#444444",
weight = 1,
smoothFactor = 0.5,
opacity = 1.0,
fillOpacity = 0.005,
highlightOptions = highlightOptions(color = "white",
weight = 3,))
```
### 32. Finally We Look at Police Districts with Crime Clustering
```{r, include=TRUE}
xxx
```
***
- Review crimes by each of 6 police districts.
### 33. Food for Thought
- Need to collect more data for greater understanding of crime parameters.
- This data set has close to 8,000 instances of "FIREARM" defined crime. Where are the locations?
- Need to plot heroine and cocaine locations to see overlaps.
- There is no gang data available since 2012. St Louis does not have a Gang Division. Does it need one?
- UCR reporting structure is poorly constructed for nation as a whole. Data is inconsistent and ill defined.